Methods and materials for identifying malignant skin lesions

ABSTRACT

This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 14/442,673, filed May 13, 2015, pending, which is a National Stage application under 35 U.S.C. § 371 of International Patent Application PCT/US2013/053982, having an International filing date of Aug. 7, 2013, designating the United States of America and published in English as International Patent Publication WO 2014/077915 A1 on May 22, 2014, which claims the benefit under Article 8 of the Patent Cooperation Treaty to U.S. Provisional Patent Application Ser. No. 61/726,217, filed Nov. 14, 2012, the disclosure of each of which is hereby incorporated herein in its entirety by this reference.

STATEMENT ACCORDING TO 37 C.F.R. § 1.821(c) or (e)—REQUEST TO TRANSFER COMPUTER-READABLE FORM OF SEQUENCE LISTING FROM PARENT APPLICATION

Pursuant to 37 C.F.R. § 1.821(c) or (e), the transmittal documents of this application include a Request to Transfer Computer-Readable Form of the Sequence Listing from the parent application, the contents of the Sequence Listing are incorporated herein by this reference.

TECHNICAL FIELD

This document relates to methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document relates to methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.

BACKGROUND

Malignant skin lesions are typically identified by obtaining a skin biopsy and morphologically assessing the biopsy's melanocytes under a microscope. Such a procedure can be difficult to standardize and can lead to overcalling of melanomas.

Once a diagnosis of melanoma is made by morphological assessment, the risk of metastasis is typically determined by the invasion depth of malignant cells into the skin (i.e., the Breslow depth). The Breslow depth can dictate further work-up such as a need for an invasive sentinel lymph node (SLN) procedure. Such procedures, however, can lead to inaccurate determinations of the true malignant potential of a pigmented lesion.

BRIEF SUMMARY

Provided are methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.

As described herein, quantitative PCR can be performed using a routine skin biopsy sample (e.g., a paraffin-embedded tissue biopsy) to obtain expression data (e.g., gene copy numbers) for one or more marker genes. Correction protocols can be used to reduce the impact of basal keratinocyte contamination on the analysis of the expression data from the test sample. For example, the contribution of gene expression from basal keratinocytes present within the test skin sample can be determined and removed from the overall gene expression values to determine the final gene expression value for a particular gene as expressed from cells other than basal keratinocytes (e.g., melanocytes). An assessment of the final gene expression values, which include minimal, if any, contribution from basal keratinocytes, for a collection of marker genes can be used to determine the benign or malignant biological behavior of the tested skin lesion.

In general, one aspect hereof features a method for identifying a malignant skin lesion. The method comprises, or consists essentially of, (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, and CXCL1 to obtain a measured expression level of the marker gene for the test sample, (b) determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample, (c) removing, from the measured expression level of the marker gene for the test sample, a level of expression attributable to keratinocytes present in the test sample using the measured expression level of the keratinocyte marker gene for the test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for the test sample, and (d) identifying the test sample as containing a malignant skin lesion based, at least in part, on the corrected value of marker gene expression for the test sample. The keratinocyte marker gene can be K14. The marker gene can be SPP1. The step (c) can comprise (i) multiplying the measured expression level of the keratinocyte marker gene for the test sample by the keratinocyte correction factor to obtain a correction value and (ii) subtracting the correction value from the measured expression level of the marker gene for the test sample to obtain the corrected value of marker gene expression for the test sample.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an exemplary process for determining the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).

FIG. 2 is a flow chart of an exemplary process for determining a keratinocyte correction factor for a marker gene of interest.

FIG. 3 is a flow chart of an exemplary process for removing copy number contamination from basal keratinocytes from a copy number value for a marker gene to determine the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for that marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).

FIG. 4 is a diagram of an example of a generic computer device and a generic mobile computer device that can be used as described herein.

FIG. 5 is a flow chart of an exemplary process for using FN1 and SPP1 expression levels to determine the benign or malignant nature of a skin lesion.

FIG. 6 is a flow chart of an exemplary process for using FN1 and ITGB3 expression levels to determine the benign or malignant nature of a skin lesion.

FIG. 7 is a network diagram.

DETAILED DESCRIPTION

This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions.

FIG. 1 shows an exemplary process 100 for determining a gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample). The process begins at box 102, where quantitative PCR using a collection of primer sets and a test sample is used to obtain a Ct value for the target of each primer set. Each gene of interest can be assessed using a single primer set or multiple different primer sets (e.g., two, three, four, five, six, seven, or more different primer sets). In some cases, quantitative PCR is performed using each primer set and control nucleic acid of the target of each primer set (e.g., linearized cDNA fragments) to obtain a standard curve for each primer set as set forth in box 104. In some cases, quantitative PCR is performed using each primer set and a known sample as an internal control (e.g., a stock biological sample) to obtain an internal control value for each primer set as set forth in box 106. This internal control can be used to set values for each primer set across different assays. In some cases, the quantitative PCR performed according to boxes 102, 104, and 106 can be performed in parallel. For example, the quantitative PCR performed according to boxes 102, 104, and 106 can be performed in a single 96 well format.

At box 108, the quality of the obtained standard curves can be confirmed. In some cases, a gene of interest included in the assay format can be a melanocyte marker (e.g., levels of MLANA and/or MITF expression) to confirm the presence of melanocytes in the test sample. Other examples of melanocyte markers that can be used as described herein include, without limitation, TYR, TYRP1, DCT, PMEL, OCA2, MLPH, and MC1R.

At box 110, the raw copy number of each target present in the test sample is determined using the Ct values and the standard curve for each target. In some cases, the averaged, corrected copy number for each gene is calculated using the raw copy number of each target of a particular gene and the internal control value for each primer set (box 112). This averaged, corrected copy number value for each gene can be normalized to a set number of one or more housekeeping genes as set forth in box 114. For example, each averaged, corrected copy number value for each gene can be normalized to 100,000 copies of the combination of ACTB, RPL8, RPLP0, and B2M. Other examples of housekeeping genes that can be used as described herein include, without limitation, RRN18S, GAPD, PGK1, PPIA, RPL13A, YWHAZ, SDHA, TFRC, ALAS1, GUSB, HMBS, HPRT1, TBP, and TUPP. Once normalized, the copy number values for each gene can be referred to as the averaged, corrected, normalized copy number for that gene as present in the test sample.

At box 116, the averaged, corrected, normalized copy number for each gene can be adjusted to remove the copy number contamination from basal keratinocytes present in the test sample. In general, copy number contamination from basal keratinocytes can be removed by (a) determining a keratinocyte correction factor for the gene of interest using one or more keratinocyte markers (e.g., keratin 14 (K14)) and one or more normal skin samples (e.g., FFPE-embedded normal skin samples), (b) determining the averaged, corrected, normalized copy number value for the one or more keratinocyte markers of the test sample and multiplying that value by the keratinocyte correction factor to obtain a correction value for the gene of interest, and (c) subtracting that correction value from the averaged, corrected, normalized copy number value of the gene of interest to obtain the final copy number for the gene of interest. Examples of keratinocyte markers that can be used as described herein include, without limitation, KRT5, KRT1, KRT10, KRT17, ITGB4, ITGA6, PLEC, DST, and COL17A1.

With reference to FIG. 2, process 200 can be used to obtain a keratinocyte correction factor for a gene of interest. At box 202, the averaged, corrected, normalized copy number for one or more genes of interest (e.g., Gene X) and one or more basal keratinocyte marker genes (e.g., K14) are determined using one or more normal skin samples and procedures similar to those described in FIG. 1. As box 204, the keratinocyte correction factor for each gene of interest (e.g., Gene X) is determined by dividing the averaged, corrected, normalized copy number for each gene of interest present in a normal skin sample by the averaged, corrected, normalized copy number of a basal keratinocyte marker gene present in a normal skin sample. Examples of keratinocyte correction factors for particular genes of interest are set forth in Table E under column “AVG per copy K14.”

With reference to FIG. 3, once a keratinocyte correction factor in determined for a particular gene of interest (e.g., Gene X), then the averaged, corrected, normalized copy number for the basal keratinocyte marker gene present in the test sample can be multiplied by the keratinocyte correction factor for the gene of interest (e.g., Gene X) to obtain a correction value for the gene of interest (e.g., Gene X). See, e.g., box 302. At box 304, the correction value for the gene of interest (e.g., Gene X) is subtracted from the averaged, corrected, normalized copy number for the gene of interest (e.g., Gene X) present in the test sample to obtain a final copy number value of the gene of interest (e.g., Gene X) present in the test sample.

FIG. 4 is a diagram of an example of a generic computer device 1400 and a generic mobile computer device 1450, which may be used with the techniques described herein. Computing device 1400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device 1450 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.

Computing device 1400 includes a processor 1402, memory 1404, a storage device 1406, a high-speed interface 1408 connecting to memory 1404 and high-speed expansion ports 1410, and a low speed interface 1415 connecting to low speed bus 1414 and storage device 1406. Each of the components 1402, 1404, 1406, 1408, 1410, and 1415, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 1402 can process instructions for execution within the computing device 1400, including instructions stored in the memory 1404 or on the storage device 1406 to display graphical information for a GUI on an external input/output device, such as display 1416 coupled to high speed interface 1408. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 1400 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The memory 1404 stores information within the computing device 1400. In one implementation, the memory 1404 is a volatile memory unit or units. In another implementation, the memory 1404 is a non-volatile memory unit or units. The memory 1404 may also be another form of computer-readable medium, such as a magnetic or optical disk.

The storage device 1406 is capable of providing mass storage for the computing device 1400. In one implementation, the storage device 1406 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer- or machine-readable medium, such as the memory 1404, the storage device 1406, memory on processor 1402, or a propagated signal.

The high speed controller 1408 manages bandwidth-intensive operations for the computing device 1400, while the low speed controller 1415 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 1408 is coupled to memory 1404, display 1416 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 1410, which may accept various expansion cards (not shown). In the implementation, low-speed controller 1415 is coupled to storage device 1406 and low-speed expansion port 1414. The low-speed expansion port, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, or wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, an optical reader, a fluorescent signal detector, or a networking device such as a switch or router, e.g., through a network adapter.

The computing device 1400 may be implemented in a number of different forms, as shown in FIG. 4. For example, it may be implemented as a standard server 1420, or multiple times in a group of such servers. It may also be implemented as part of a rack server system 1424. In addition, it may be implemented in a personal computer such as a laptop computer 1422. In some cases, components from computing device 1400 may be combined with other components in a mobile device (not shown), such as device 1450. Each of such devices may contain one or more of computing device 1400, 1450, and an entire system may be made up of multiple computing devices 1400, 1450 communicating with each other.

Computing device 1450 includes a processor 1452, memory 1464, an input/output device such as a display 1454, a communication interface 1466, and a transceiver 1468, among other components (e.g., a scanner, an optical reader, a fluorescent signal detector). The device 1450 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the components 1450, 1452, 1464, 1454, 1466, and 1468, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

The processor 1452 can execute instructions within the computing device 1450, including instructions stored in the memory 1464. The processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor may provide, for example, for coordination of the other components of the device 1450, such as control of user interfaces, applications run by device 1450, and wireless communication by device 1450.

Processor 1452 may communicate with a user through control interface 1458 and display interface 1456 coupled to a display 1454. The display 1454 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 1456 may comprise appropriate circuitry for driving the display 1454 to present graphical and other information to a user. The control interface 1458 may receive commands from a user and convert them for submission to the processor 1452. In addition, an external interface 1462 may be provide in communication with processor 1452, so as to enable near area communication of device 1450 with other devices. External interface 1462 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

The memory 1464 stores information within the computing device 1450. The memory 1464 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 1474 may also be provided and connected to device 1450 through expansion interface 1472, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 1474 may provide extra storage space for device 1450, or may also store applications or other information for device 1450. For example, expansion memory 1474 may include instructions to carry out or supplement the processes described herein, and may include secure information also. Thus, for example, expansion memory 1474 may be provide as a security module for device 1450, and may be programmed with instructions that permit secure use of device 1450. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer- or machine-readable medium, such as the memory 1464, expansion memory 1474, memory on processor 1452, or a propagated signal that may be received, for example, over transceiver 1468 or external interface 1462.

Device 1450 may communicate wirelessly through communication interface 1466, which may include digital signal processing circuitry where necessary. Communication interface 1466 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 1468. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 1470 may provide additional navigation- and location-related wireless data to device 1450, which may be used as appropriate by applications running on device 1450.

Device 1450 may also communicate audibly using audio codec 1460, which may receive spoken information from a user and convert it to usable digital information. Audio codec 1460 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 1450. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 1450.

The computing device 1450 may be implemented in a number of different forms, as shown in FIG. 4. For example, it may be implemented as a cellular telephone 1480. It may also be implemented as part of a smartphone 1482, personal digital assistant, or other similar mobile device.

Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described herein can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network).

Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES Example 1—Marker Genes that Discriminate Between Benign and Malignant Tissue

Marker genes were ordered by their ability to differentiate benign from malignant tissue (Table A). This was based on the analysis of 73 benign and 53 malignant tissues, and the hypothesis that changes in expression of fibronectin-associated gene networks are indicative of malignant cell behavior. Values of the test statistic were for the Wilcoxon rank sum test. The values of the test statistic for a Winsorized two-sample test (trimmed outliers were replaced with actual values) and for the chi-square test for the zero vs. >zero versions of each variable were included. The top 5 discriminatory genes based on each statistical test were highlighted in bold.

TABLE A Test statistic value Wilcoxon rank Winsorized two- gene sum test sample t-test Chi- square test FN1 −10.2312 −8.04081 106.714 SPP1 −9.0279 −4.9374 86.774 COL4A1 −8.8807 −7.27171 83.711 TNC −8.7511 −8.31049 75.549 ITGA3 −8.6008 −5.86334 79.788 LOXL3 −8.1978 −6.75327 75.144 AGRN −8.1243 −7.91238 62.611 VCAN −8.0812 −6.24088 67.388 PLOD3 −8.0384 −6.89248 62.691 ITGB1 −8.0021 −7.38143 59.973 PTK2 −7.5279 −7.19889 54.446 CTGF −7.4997 −5.581 57.79 PLOD1 −7.332 −7.36126 44.87 LAMC1 −7.2425 −6.1057 54.233 THBS1 −7.2425 −5.60331 54.233 LOXL2 −7.2241 −6.33208 55.909 IL6 −7.1777 −6.41883 56.966 LOXL1 −7.1279 −6.34431 52.878 IL8 −7.1194 −5.76042 57.296 CYR61 −6.741 −6.97388 43.866 ITGAV −6.5947 −6.27571 47.021 YAP −6.4848 −6.36431 42.417 BGN −6.3419 −6.01066 25.387 LAMB1 −6.3293 −5.68826 37.061 ITGB3 −6.3142 −5.13158 40.835 CXCL1 −6.1077 −5.66564 40.137 THBS2 −6.0427 −5.02003 37.413 COL18A1 −6.0379 −4.9125 41.339 SPARC −6.0272 −6.39324 38.098 TP53 −6.0182 −6.18554 34.945 PLOD2 −5.9082 −3.50272 47.576 CCL2 −5.8844 −5.38758 30.69 FBLN2 −5.5848 −4.59826 31.913 LAMA1 −5.4876 −4.2817 31.071 THBS4 −5.3971 −3.88786 35.27 COL1A1 −5.325 −4.37617 34.693 ITGA5 −4.9847 −3.56695 25.243 TAZ −4.036 −3.26011 18.313 POSTN −3.8054 −2.78378 19.813 LOX −3.728 −2.8677 17.157 CSRC −3.7078 −3.71759 13.983 LAMA3 −3.5805 −2.99652 13.391 CDKN1A −3.5766 −3.20447 17.228 CDKN2A −3.5491 −2.90903 15.938 ITGA2 −3.4083 −2.72495 11.766 LAMC2 −3.4083 −2.53784 11.766 PCOLCE2 −3.3469 −3.53676 14.449 LOXL4 −3.2079 −2.76128 10.943 PCOLCE −2.2172 −1.13805 7.993 LAMB3 −1.2822 0.89459 7.028 CSF2 2.175 1.93095 4.522

Example 2—Marker Panel Revision after Statistical Analysis

The candidate gene list from Example 1 was modified to include other FN1 network genes as well as four housekeeping genes (ACTB, RPLP0, RPL8, and B2M), two keratinocyte markers (K10 and K14) to assess keratinocyte contamination, and four melanocyte markers (MITF, TYR, MLANA and PMEL) to assess melanocyte content in the skin sections. Genes from Example 1 with low discriminatory value and a more distant neighborhood to FN1 were excluded from the test setup (LAMC1, LOXL2, CYR61, YAP, BGN, LAMB1, THBS2, COL18A1, SPARC, TP53, PLOD2, CCL2, FBLN2, LAMA1, THBS4, COL1A1, TAZ, POSTN, LOX, CSRC, LAMA3, CDKN1A, CDKN2A, LAMC2, PCOLCE2, LOXL4, PCOLCE, LAMB3, and CSF2). Instead, the discriminatory ability of other FN1 network genes was determined (PLAT, CSK, GDF15, FARP1, ARPC1B, NES, NTRK3, SNX17, L1CAM, and CD44). The following results were based on the analysis of 26 benign nevi and 52 primary cutaneous melanomas with documented subsequent metastasis or skin lesions of melanoma metastasis (Table B). The top 5 genes were highlighted.

TABLE B Test Statistic value Wilcoxon Winsorized gene rank sum test two-sample t-test Chi-square test COL4A1 −5.85975 −5.42545 46.3273 FN1 −5.50862 −3.63639 35.1951 PLAT −4.82670 −3.13568 25.7234 IL8 −4.61443 −4.41668 28.6000 SPP1 −4.60153 −3.08137 23.0816 PLOD3 −4.37001 −3.91553 18.8036 TNC −4.26431 −3.14128 19.5000 CXCL1 −4.24452 −3.76681 20.6471 CSK −4.15178 −2.96444 18.3962 GDF15 −4.01364 −2.99752 13.7083 ITGB3 −3.92608 −2.80068 16.3091 CCL2 −3.61870 −3.45423 17.5176 VCAN −3.46906 −2.26781 12.5593 ITGB1 −3.40897 −3.63399 5.0221 PLOD1 −3.40380 −3.20309 9.2625 CTGF −3.11725 −2.20507 10.0645 THBS1 −3.11721 −2.01257 10.0645 ITGA3 −3.04915 −2.65398 7.5341 FARP1 −2.99724 −2.28024 9.2857 AGRN −2.92104 −3.30679 1.8838 IL6 −2.85960 −3.05600 10.6257 LOXL3 −2.84999 −2.70498 5.1096 LOXL1 −2.69957 −2.11477 8.1250 ARPC1B −2.57571 −2.82320 All but 1 value > 0 NES −2.45264 −2.70056 2.4375 PTK2 −2.22328 −2.26180 4.4057 ITGA2 −2.08353 −1.50078 4.4571 ITGA5 −1.93478 −1.39663 3.8451 ITGAV −1.29341 −0.81964 3.5615 NTRK3 −1.22485 75 of the 78 values are = 0 MITF 0.58305 0.73916 0.4274 SNX17 0.74754 0.90733 0.0785 L1CAM 1.61125 0.27151 2.1081 MLANA 2.96258 2.92548 All values > 0 CD44 5.23089 7.17590 All but 1 value > 0

Based on the results of Example 1 and above, FN1 was identified as a component of the melanoma phenotype that is at the core of a gene network that discriminates between benign and malignant melanocytic skin lesions (FIG. 7). The modeling was based on the STRING 9.0 database (string-db.org).

The list of all 71 genes tested is provided in Table 1.

TABLE 1 List of genes used to discriminate benign skin tissue lesions from malignant skin tissue lesions. Gene Name GenBank ® Accession No. GenBank ® GI No. FN1 NM_212482 47132556 NM_002026 47132558 NM_212474 47132548 NM_212476 47132552 NM_212478 47132554 NM_054034 47132546 SPP1 NM_001040058 91206461 NM_001040060 91598938 NM_000582 38146097 COL4A1 NM_001845 148536824 TNC NM_002160 340745336 ITGA3 NM_005501 171846264 NM_002204 171846266 LOXL3 NM_032603 22095373 AGRN NM_198576 344179122 VCAN NM_004385 255918074 NM_001164098 255918078 NM_001164097 255918076 PLOD3 NM_001084 62739167 ITGB1 NM_002211 182519230 NM_133376 182507162 NM_033668 182507160 PTK2 NM_001199649 313851043 NM_005607 313851042 NM_153831 313851041 CTGF NM_001901 98986335 PLOD1 NM_000302 324710986 LAMC1 NM_002293 145309325 THBS1 NM_003246 40317625 LOXL2 NM_002318 67782347 IL6 NM_000600 224831235 LOXL1 NM_005576 67782345 IL8 NM_000584 324073503 CYR61 NM_001554 197313774 ITGAV NM_001144999 223468594 NM_001145000 223468596 NM_002210 223468593 YAP NM_001130145 303523503 NM_001195045 303523626 NM_006106 303523510 NM_001195044 303523609 BGN NM_001711 268607602 LAMB1 NM_002291 167614503 ITGB3 NM_000212 47078291 CXCL1 NM_001511 373432598 THBS2 NM_003247 40317627 COL18A1 NM_030582 110611234 NM_130445 110611232 SPARC NM_003118 365777426 TP53 NM_000546 371502114 NM_001126112 371502115 NM_001126114 371502117 NM_001126113 371502116 PLOD2 NM_182943 62739164 NM_000935 62739165 CCL2 NM_002982 56119169 FBLN2 NM_001998 51873054 NM_001004019 51873052 NM_001165035 259013546 LAMA1 NM_005559 329112585 THBS4 NM_003248 291167798 COL1A1 NM_000088 110349771 ITGA5 NM_002205 56237028 TAZ NM_000116 195232764 NM_181311 195232766 NM_181312 195232765 NM_181313 195232767 POSTN NM_001135934 209862910 NM_006475 209862906 NM_001135935 209863010 LOX NM_001178102 296010939 NM_002317 296010938 CSRC NM_005417 38202215 NM_198291 38202216 LAMA3 NM_198129 38045909 NM_001127717 189217424 CDKN1A NM_000389 310832422 NM_001220777 334085239 NM_078467 310832423 NM_001220778 334085241 CDKN2A NM_000077 300863097 NM_058195 300863095 NM_001195132 304376271 ITGA2 NM_002203 116295257 LAMC2 NM_005562 157419137 NM_018891 157419139 PCOLCE2 NM_013363 296317252 LOXL4 NM_032211 67782348 PCOLCE NM_002593 157653328 LAMB3 NM_000228 62868214 NM_001017402 62868216 NM_001127641 189083718 CSF2 NM_000758 371502128 ACTB NM_001101 168480144 RPLP0 NM_053275 49087137 NM_001002 49087144 RPL8 NM_000973 72377361 NM_033301 15431305 B2M NM_004048 37704380 K10 NM_000421 195972865 K14 NM_000526 197313720 MITF NM_198158 296841082 NM_198177 296841080 NM_006722 296841079 NM_198159 296841078 NM_000248 296841081 NM_001184967 296841084 NM_198178 296923803 TYR NM_000372 113722118 MLANA NM_005511 5031912 PMEL NM_001200054 318037594 NM_001200053 318037592 NM_006928 318068057 NES NM_006617 38176299 L1CAM NM_024003 221316758 NM_001143963 221316759 NM_000425 221316755 GDF15 NM_004864 153792494 ARPC1B NM_005720 325197176 FARP1 NM_005766 48928036 NM_001001715 159032536 NTRK3 NM_001007156 340745351 NM_001012338 340745349 NM_001243101 340745352 NM_002530 340745350 CSK NM_001127190 187475372 NM_004383 187475371 CD44 NM_001001391 48255940 NM_001001392 48255942 NM_001202556 321400139 NM_001001389 48255936 NM_000610 48255934 NM_001001390 48255938 NM_001202555 321400137 NM_001202557 321400141 SNX17 NM_014748 388596703 PLAT NM_000930 132626665 NM_033011 132626641

Gene expression of target genes was assessed by SYBR/EVA-Green based RT-PCR. All tested genes were accompanied by a standard curve for quantification of absolute copy number per a defined number of housekeeping genes. mRNA extraction from paraffin-embedded biospecimen was performed using an extraction protocol (Qiagen RNA FFPE extraction kit) and an extraction robot (Qiacube from Qiagen). mRNA was transcribed into cDNA using a commercially available kit (iScript kit from BioRad), and Fluidigm technology was used for PCR cycling.

The primer design was performed using web-based open access software. The primers were HPLC purified to minimize background and were optimized for formalin-fixed, paraffin-embedded (FFPE) tissue (i.e., highly degraded tissue). The primers were designed to detect a maximum number of gene transcripts and were designed to be cDNA specific (i.e., not affected by genomic DNA contamination of the total, tissue-derived cDNA). The housekeeping genes, keratin genes, melanocyte-specific genes, and selected high interest genes were detected using four separate and individually designed primer pairs. The primer pairs are set forth in Table 2.

TABLE 2 Primer sets for indicated genes. Gene Name Forward primer Reverse primer ACTB 5′-GCCAACCGCGAGAAGATG-3′; 5′-GGCTGGGGTGTTGAAGGT-3′; SEQ ID NO: 1 SEQ ID NO: 2 5′-CGCGAGAAGATGACCCAGAT-3′; 5′-GGGGTGTTGAAGGTCTCAAA-3′; SEQ ID NO: 3 SEQ ID NO: 4 5′-TGACCCAGATCATGTTTGAGA-3′; 5′-GTACATGGCTGGGGTGTTG-3′; SEQ ID NO: 5 SEQ ID NO: 6 5′-CTGAACCCCAAGGCCAAC-3′; 5′-TGATCTGGGTCATCTTCTCG-3′; SEQ ID NO: 7 SEQ ID NO: 8 RPLP0 5′-AACTCTGCATTCTCGCTTCC-3′; 5′-GCAGACAGACACTGGCAACA-3′; SEQ ID NO: 9 SEQ ID NO: 10 5′-GCACCATTGAAATCCTGAGTG-3′; 5′-GCTCCCACTTTGTCTCCAGT-3′; SEQ ID NO: 11 SEQ ID NO: 12 5′-TCACAGAGGAAACTCTGCATTC-3′; 5′-GGACACCCTCCAGGAAGC-3′; SEQ ID NO: 13 SEQ ID NO: 14 5′-ATCTCCAGGGGCACCATT-3′; 5′-AGCTGCACATCACTCAGGATT-3′; SEQ ID NO: 15 SEQ ID NO: 16 RPL8 5′-ACTGCTGGCCACGAGTACG-3′; 5′-ATGCTCCACAGGATTCATGG-3′; SEQ ID NO: 17 SEQ ID NO: 18 5′-ACAGAGCTGTGGTTGGTGTG-3′; 5′-TTGTCAATTCGGCCACCT-3′; SEQ ID NO: 19 SEQ ID NO: 20 5′-TATCTCCTCAGCCAACAGAGC-3′; 5′-AGCCACCACACCAACCAC-3′; SEQ ID NO: 21 SEQ ID NO: 22 5′-GTGTGGCCATGAATCCTGT-3′; 5′-CCACCTCCAAAAGGATGCTC-3′; SEQ ID NO: 23 SEQ ID NO: 24 B2M 5′-TCTCTCTTTCTGGCCTGGAG-3′; 5′-GAATCTTTGGAGTACGCTGGA-3′; SEQ ID NO: 25 SEQ ID NO: 26 5′-TGGAGGCTATCCAGCGTACT-3′; 5′-CGTGAGTAAACCTGAATCTTTGG-3′; SEQ ID NO: 27 SEQ ID NO: 28 5′-CCAGCGTACTCCAAAGATTCA-3′; 5′-TCTCTGCTGGATGACGTGAG-3′; SEQ ID NO: 29 SEQ ID NO: 30 5′-GGCTATCCAGCGTACTCCAA-3′; 5′-GCTGGATGACGTGAGTAAACC-3′; SEQ ID NO: 31 SEQ ID NO: 32 KRT14 5′-ACCATTGAGGACCTGAGGAA-3′; 5′-GTCCACTGTGGCTGTGAGAA-3′; SEQ ID NO: 33 SEQ ID NO: 34 5′-CATTGAGGACCTGAGGAACA-3′; 5′-AATCTGCAGAAGGACATTGG-3′; SEQ ID NO: 35 SEQ ID NO: 36 5′-GATGACTTCCGCACCAAGTA-3′; 5′-CGCAGGTTCAACTCTGTCTC-3′; SEQ ID NO: 37 SEQ ID NO: 38 5′-TCCGCACCAAGTATGAGACA-3′; 5′-ACTCATGCGCAGGTTCAACT-3′; SEQ ID NO: 39 SEQ ID NO: 40 KRT10 5′-GAGCCTCGTGACTACAGCAA-3′; 5′-GCAGGATGTTGGCATTATCAGT-3′; SEQ ID NO: 41 SEQ ID NO: 42 5′-AAAACCATCGATGACCTTAAAAA-3′; 5′-GATCTGAAGCAGGATGTTGG-3′; SEQ ID NO: 43 SEQ ID NO: 44 MITF 5′-TTCCCAAGTCAAATGATCCAG-3′; 5′-AAGATGGTTCCCTTGTTCCA-3′; SEQ ID NO: 45 SEQ ID NO: 46 5′-CGGCATTTGTTGCTCAGAAT-3′; 5′-GAGCCTGCATTTCAAGTTCC-3′; SEQ ID NO: 47 SEQ ID NO: 48 TYR 5′-TTCCTTCTTCACCATGCATTT-3′; 5′-GGAGCCACTGCTCAAAAATA-3′; SEQ ID NO: 49 SEQ ID NO: 50 5′-TCCAAAGATCTGGGCTATGA-3′; 5′-TTGAAAAGAGTCTGGGTCTGAA-3′; SEQ ID NO: 51 SEQ ID NO: 52 MLANA 5′-GAGAAAAACTGTGAACCTGTGG-3′; 5′-ATAAGCAGGTGGAGCATTGG-3′; SEQ ID NO: 53 SEQ ID NO: 54 5′-GAAGACGAAATGGATACAGAGC-3′; 5′-GTGCCAACATGAAGACTTTTATC-3′; SEQ ID NO: 55 SEQ ID NO: 56 PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′; 5′-CCAAGGCCTGCTTCTTGAC-3′; SEQ ID NO: 57 SEQ ID NO: 58 5′-GCTGTGGTCCTTGCATCTCT-3′; 5′-GCTTCATAAGTCTGCGCCTA-3′; SEQ ID NO: 59 SEQ ID NO: 60 FN1 5′-CTCCTGCACATGCTTTGGA-3′; 5′-AGGTCTGCGGCAGTTGTC-3′; SEQ ID NO: 61 SEQ ID NO: 62 5′-AGGCTTTGGAAGTGGTCATT-3′; 5′-CCATTGTCATGGCACCATCT-3′; SEQ ID NO: 63 SEQ ID NO: 64 5′-GAAGTGGTCATTTCAGATGTGATT-3′; 5′-CCATTGTCATGGCACCATCT-3′; SEQ ID NO: 65 SEQ ID NO: 66 5′-TGGTCATTTCAGATGTGATTCAT-3′; 5′-CATTGTCATGGCACCATCTA-3′; SEQ ID NO: 67 SEQ ID NO: 68 SPP1 5′-GTTTCGCAGACCTGACATCC-3′; 5′-TCCTCGTCTGTAGCATCAGG-3′; SEQ ID NO: 69 SEQ ID NO: 70 5′-CCTGACATCCAGTACCCTGA-3′; 5′-TGAGGTGATGTCCTCGTCTG-3′; SEQ ID NO: 71 SEQ ID NO: 72 5′-GAATCTCCTAGCCCCACAGA-3′; 5′-GGTTTCTTCAGAGGACACAGC-3′; SEQ ID NO: 73 SEQ ID NO: 74 5′-CCCATCTCAGAAGCAGAATCTC-3′; 5′-ACAGCATTCTGTGGGGCTA-3′; SEQ ID NO: 75 SEQ ID NO: 76 COL4A1 5′-GGAAAACCAGGACCCAGAG-3′; 5′-CTTTTTCCCCTTTGTCACCA-3′; SEQ ID NO: 77 SEQ ID NO: 78 5′-AGAAAGGTGAACCCGGAAAA-3′; 5′-GGTTTGCCTCTGGGTCCT-3′; SEQ ID NO: 79 SEQ ID NO: 80 5′-GAGAAAAGGGCCAAAAAGGT-3′; 5′-CATCCCCTGAAATCCAGGTT-3′; SEQ ID NO: 81 SEQ ID NO: 82 5′-AAAGGGCCAAAAAGGTGAAC-3′; 5′-CCTGGCATCCCCTGAAAT-3′; SEQ ID NO: 83 SEQ ID NO: 84 TNC 5′-GTGTCAACCTGATGGGGAGA-3′; 5′-GTTAACGCCCTGACTGTGGT-3′; SEQ ID NO: 85 SEQ ID NO: 86 5′-GGTACAGTGGGACAGCAGGT-3′; 5′-GATCTGCCATTGTGGTAGGC-3′; SEQ ID NO: 87 SEQ ID NO: 88 5′-AACCACAGTCAGGGCGTTA-3′; 5′-GTTCGTGGCCCTTCCAGT-3′; SEQ ID NO: 89 SEQ ID NO: 90 5′-AAGCTGAAGGTGGAGGGGTA-3′; 5′-GAGTCACCTGCTGTCCCACT-3′; SEQ ID NO: 91 SEQ ID NO: 92 ITGA3 5′-TATTCCTCCGAACCAGCATC-3′; 5′-CACCAGCTCCGAGTCAATGT-3′; SEQ ID NO: 93 SEQ ID NO: 94 5′-CCACCATCAACATGGAGAAC-3′; 5′-AGTCAATGTCCACAGAGAACCA-3′; SEQ ID NO: 95 SEQ ID NO: 96 LOXL3 5′-CAACTGCCACATTGGTGATG-3′; 5′-AAACCTCCTGTTGGCCTCTT-3′; SEQ ID NO: 97 SEQ ID NO: 98 5′-TGACATCACGGATGTGAAGC-3′; 5′-GGGTTGATGACAACCTGGAG-3′; SEQ ID NO: 99 SEQ ID NO: 100 AGRN 5′-TGTGACCGAGAGCGAGAAG-3′; 5′-CAGGCTCAGTTCAAAGTGGTT-3′; SEQ ID NO: 101 SEQ ID NO: 102 5′-CGGACCTTTGTCGAGTACCT-3′; 5′-GTTGCTCTGCAGTGCCTTCT-3′; SEQ ID NO: 103 SEQ ID NO: 104 VCAN 5′-GACTTCCGTTGGACTGATGG-3′; 5′-TGGTTGGGTCTCCAATTCTC-3′; SEQ ID NO: 105 SEQ ID NO: 106 5′-ACGTGCAAGAAAGGAACAGT-3′; 5′-TCCAAAGGTCTTGGCATTTT-3′; SEQ ID NO: 107 SEQ ID NO: 108 PLOD3 5′-GCAGAGATGGAGCACTACGG-3′; 5′-CAGCCTTGAATCCTCATGC-3′; SEQ ID NO: 109 SEQ ID NO: 110 5′-GGAAGGAATCGTGGAGCAG-3′; 5′-CAGCAGTGGGAACCAGTACA-3′; SEQ ID NO: 111 SEQ ID NO: 112 ITGB1 5′-CTGATGAATGAAATGAGGAGGA-3′; 5′-CACAAATGAGCCAAATCCAA-3′; SEQ ID NO: 113 SEQ ID NO: 114 5′-CAGTTTGCTGTGTGTTTGCTC-3′; 5′-CATGATTTGGCATTTGCTTTT-3′; SEQ ID NO: 115 SEQ ID NO: 116 PTK2 5′-GCCCCACCAGAGGAGTATGT-3′; 5′-AAGCCGACTTCCTTCACCA-3′; SEQ ID NO: 117 SEQ ID NO: 118 5′-GAGACCATTCCCCTCCTACC-3′; 5′-GCTTCTGTGCCATCTCAATCT-3′; SEQ ID NO: 119 SEQ ID NO: 120 CTGF 5′-CGAAGCTGACCTGGAAGAGA-3′; 5′-TGGGAGTACGGATGCACTTT-3′; SEQ ID NO: 121 SEQ ID NO: 122 5′-GTGTGCACCGCCAAAGAT-3′; 5′-CGTACCACCGAAGATGCAG-3′; SEQ ID NO: 123 SEQ ID NO: 124 PLOD1 5′-CTACCCCGGCTACTACACCA-3′; 5′-GACAAAGGCCAGGTCAAACT-3′; SEQ ID NO: 125 SEQ ID NO: 126 5′-AGTCGGGGTGGATTACGAG-3′; 5′-ACAGTTGTAGCGCAGGAACC-3′; SEQ ID NO: 127 SEQ ID NO: 128 LAMC1 5′-ATGATGATGGCAGGGATGG-3′; 5′-GCATTGATCTCGGCTTCTTG-3′; SEQ ID NO: 129 SEQ ID NO: 130 THBS1 5′-CTGTGGCACACAGGAAACAC-3′; 5′-ACGAGGGTCATGCCACAG-3′; SEQ ID NO: 131 SEQ ID NO: 132 5′-GCCAAAGACGGGTTTCATTA-3′; 5′-GCCATGATTTTCTTCCCTTC-3′; SEQ ID NO: 133 SEQ ID NO: 134 LOXL2 5′-CTCCTCCTACGGCAAGGGA-3′; 5′-TGGAGATTGTCTAACCAGATGGG-3′; SEQ ID NO: 135 SEQ ID NO: 136 5′-CTCCTACGGCAAGGGAGAAG-3′; 5′-TTGCCAGTACAGTGGAGATTG-3′; SEQ ID NO: 137 SEQ ID NO: 138 IL6 5′-CCAGAGCTGTCAGATGAGT-3′; 5′-TGCATCTAGATTCTTTGCCTTTT-3′; SEQ ID NO: 139 SEQ ID NO: 140 LOXL1 5′-AGGGCACAGCAGACTTCCT-3′; 5′-TCGTCCATGCTGTGGTAATG-3′; SEQ ID NO: 141 SEQ ID NO: 142 5′-GCATGCACCTCTCATACCC-3′; 5′-CGCATTGTAGGTGTCATAGCA-3′; SEQ ID NO: 143 SEQ ID NO: 144 IL8 5′-CTTGGCAGCCTTCCTGATT-3′; 5′-GCAAAACTGCACCTTCACAC-3′; SEQ ID NO: 145 SEQ ID NO: 146 CYR61 5′-CGCTCTGAAGGGGATCTG-3′; 5′-ACAGGGTCTGCCCTCTGACT-3′; SEQ ID NO: 147 SEQ ID NO: 148 5′-GAGCTCAGTCAGAGGGCAGA-3′; 5′-AACTTTCCCCGTTTTGGTAGA-3′; SEQ ID NO: 149 SEQ ID NO: 150 ITGAV 5′-GACCTTGGAAACCCAATGAA-3′; 5′-TCCATCTCTGACTGCTGGTG-3′; SEQ ID NO: 147 SEQ ID NO: 148 5′-GGTGGTATGTGACCTTGGAAA-3′; 5′-GCACACTGAAACGAAGACCA-3′; SEQ ID NO: 149 SEQ ID NO: 150 YAP 5′-TGAACAGTGTGGATGAGATGG-3′; 5′-GCAGGGTGCTTTGGTTGATA-3′; SEQ ID NO: 151 SEQ ID NO: 152 BGN 5′-AAGGGTCTCCAGCACCTCTAC-3′; 5′-AAGGCCTTCTCATGGATCTT-3′; SEQ ID NO: 153 SEQ ID NO: 154 5′-GAGCTCCGCAAGGATGACT-3′; 5′-AGGACGAGGGCGTAGAGGT-3′; SEQ ID NO: 155 SEQ ID NO: 156 LAMB1 5′-CATTCAAGGAACCCAGAACC-3′; 5′-GCGTTGAACAAGGTTTCCTC-3′; SEQ ID NO: 157 SEQ ID NO: 158 ITBG3 5′-AAGAGCCAGAGTGTCCCAAG-3′; 5′-ACTGAGAGCAGGACCACCA-3′; SEQ ID NO: 159 SEQ ID NO: 160 5′-CTTCTCCTGTGTCCGCTACAA-3′; 5′-CATGGCCTGAGCACATCTC-3′; SEQ ID NO: 161 SEQ ID NO: 162 5′-TGCCTGCACCTTTAAGAAAGA-3′; 5′-CCGGTCAAACTTCTTACACTCC-3′; SEQ ID NO: 163 SEQ ID NO: 164 5′-AAGGGGGAGATGTGCTCAG-3′; 5′-CAGTCCCCACAGCTGCAC-3′; SEQ ID NO: 165 SEQ ID NO: 166 CXCL1 5′-AAACCGAAGTCATAGCCACAC-3′; 5′-AAGCTTTCCGCCCATTCTT-3′; SEQ ID NO: 167 SEQ ID NO: 168 THBS2 5′-AGGCCCAAGACTGGCTACAT-3′; 5′-CTGCCATGACCTGTTTTCCT-3′; SEQ ID NO: 169 SEQ ID NO: 170 5′-GGCAGGTGCGAACCTTATG-3′; 5′-CCTTCCAGCCAATGTTCCT-3′; SEQ ID NO: 171 SEQ ID NO: 172 COL18A1 5′-GATCGCTGAGCTGAAGGTG-3′; 5′-CGGATGCCCCATCTGAGT-3′; SEQ ID NO: 173 SEQ ID NO: 174 SPARC 5′-CCCATTGGCGAGTTTGAGAAG-3′; 5′-AGGAAGAGTCGAAGGTCTTGTT-3′; SEQ ID NO: 175 SEQ ID NO: 176 5′-GGAAGAAACTGTGGCAGAGG-3′; 5′-GGACAGGATTAGCTCCCACA-3′; SEQ ID NO: 177 SEQ ID NO: 178 TP53 5′-ACAACGTTCTGTCCCCCTTG-3′; 5′-GGGGACAGCATCAAATCATC-3′; SEQ ID NO: 179 SEQ ID NO: 180 PLOD2 5′-TGGATGCAGATGTTGTTTTGA-3′; 5′-CACAGCTTTCCATGACGAGTT-3′; SEQ ID NO: 181 SEQ ID NO: 182 5′-TTGATTGAACAAAACAGAAAGATCA-3′; 5′-TGACGAGTTACAAGAGGAGCAA-3′; SEQ ID NO: 183 SEQ ID NO: 184 CCL2 5′-CTGCTCATAGCAGCCACCTT-3′; 5′-AGGTGACTGGGGCATTGATT-3′; SEQ ID NO: 185 SEQ ID NO: 186 FBLN2 5′-ACGTGGAGGAGGACACAGAC-3′; 5′-GGAGCCTTCAGGGCTACTTC-3′; SEQ ID NO: 187 SEQ ID NO: 188 LAMA1 5′-AGCACTGCCAAAGTGGATG-3′; 5′-TTGTTGACATGGAACAAGACC-3′; SEQ ID NO: 189 SEQ ID NO: 190 THBS4 5′-GTGGGCTACATCAGGGTACG-3′; 5′-CAGAGTCAGCCACCAACTCA-3′; SEQ ID NO: 191 SEQ ID NO: 192 5′-CATCATCTGGTCCAACCTCA-3′; 5′-GTCCTCAGGGATGGTGTCAT-3′; SEQ ID NO: 193 SEQ ID NO: 194 COL1A1 5′-TGACCTCAAGATGTGCCACT-3′; 5′-TGGTTGGGGTCAATCCAGTA-3′; SEQ ID NO: 195 SEQ ID NO: 196 5′-GATGGATTCCAGTTCGAGTATG-3′; 5′-ATCAGGCGCAGGAAGGTC-3′; SEQ ID NO: 197 SEQ ID NO: 198 ITGA5 5′-CCCAAAAAGAGCGTCAGGT-3′; 5′-TTGTTGACATGGAACAAGACC-3′; SEQ ID NO: 199 SEQ ID NO: 200 TAZ 5′-CTTCCTAACAGTCCGCCCTA-3′; 5′-CCCGATCAGCACAGTGATTT-3′; SEQ ID NO: 201 SEQ ID NO: 202 POSTN 5′-CTGCTTCAGGGAGACACACC-3′; 5′-TGGCTTGCAACTTCCTCAC-3′; SEQ ID NO: 203 SEQ ID NO: 204 5′-AGGAAGTTGCAAGCCAACAA-3′; 5′-CGACCTTCCCTTAATCGTCTT-3′; SEQ ID NO: 205 SEQ ID NO: 206 LOX 5′-GCGGAGGAAAACTGTCTGG-3′; 5′-AAATCTGAGCAGCACCCTGT-3′; SEQ ID NO: 207 SEQ ID NO: 208 5′-ATATTCCTGGGAATGGCACA-3′; 5′-CCATACTGTGGTAATGTTGATGA-3′; SEQ ID NO: 209 SEQ ID NO: 210 CSRC 5′-TGTCAACAACACAGAGGGAGA-3′; 5′-CACGTAGTTGCTGGGGATGT-3′; SEQ ID NO: 211 SEQ ID NO: 212 5′-TGGCAAGATCACCAGACGG-3′; 5′-GGCACCTTTCGTGGTCTCAC-3′; SEQ ID NO: 213 SEQ ID NO: 214 LAMA3 5′-CATGTCGTCTTGGCTCACTC-3′; 5′-AAATTCTGGCCCCAACAATAC-3′; SEQ ID NO: 215 SEQ ID NO: 216 CDKN1A 5′-CATGTCGTCTTGGCTCACTC-3′; 5′-AAATTCTGGCCCCAACAATAC-3′; SEQ ID NO: 217 SEQ ID NO: 218 CDKN2A 5′-AGGAGCCAGCGTCTAGGG-3′; 5′-CTGCCCATCATCATGACCT-3′; SEQ ID NO: 219 SEQ ID NO: 220 5′-AACGCACCGAATAGTTACGG-3′; 5′-CATCATCATGACCTGGATCG-3′; SEQ ID NO: 221 SEQ ID NO: 222 ITGA2 5′-CACTGTTACGATTCCCCTGA-3′; 5′-CGGCTTTCTCATCAGGTTTC-3′; SEQ ID NO: 223 SEQ ID NO: 224 LAMC2 5′-ATTAGACGGCCTCCTGCATC-3′; 5′-AGACCAGCCCCTCTTCATCT-3′; SEQ ID NO: 225 SEQ ID NO: 226 PCOLCE2 5′-TACTTGGAAAATCACAGTTCCCG-3′; 5′-TGAATCGGAAATTGAGAACGACT-3′; SEQ ID NO: 225 SEQ ID NO: 226 LOXL4 5′-GGCCCCGGGAATTATATCT-3′; 5′-CCACTTCATAGTGGGGGTTC-3′; SEQ ID NO: 227 SEQ ID NO: 228 5′-CTGCACAACTGCCACACAG-3′; 5′-GTTCTGCATTGGCTGGGTAT-3′; SEQ ID NO: 229 SEQ ID NO: 230 PCOLCE 5′-CGTGGCAAGTGAGGGGTTC-3′; 5′-CGAAGACTCGGAATGAGAGGG-3′; SEQ ID NO: 231 SEQ ID NO: 232 5′-GAGGCTTCCTGCTCTGGT-3′; 5′-CGCAAAATTGGTGCTCAGT-3′; SEQ ID NO: 233 SEQ ID NO: 234 LAMB3 5′-GTCCGGGACTTCCTAACAGA-3′; 5′-GCTGACCTCCTGGATAGTGTG-3′; SEQ ID NO: 235 SEQ ID NO: 236 PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′; 5′-CCAAGGCCTGCTTCTTGAC-3′; SEQ ID NO: 237 SEQ ID NO: 238 5′-GCTGTGGTCCTTGCATCTCT-3′; 5′-GCTTCATAAGTCTGCGCCTA-3′; SEQ ID NO: 239 SEQ ID NO: 240 NES 5′-CTTCCCTCAGCTTTCAGGAC-3′; 5′-TCTGGGGTCCTAGGGAATTG-3′; SEQ ID NO: 241 SEQ ID NO: 242 5′-ACCTCAAGATGTCCCTCAGC-3′; 5′-CAGGAGGGTCCTGTACGTG-3′; SEQ ID NO: 243 SEQ ID NO: 244 L1CAM 5′-GAGACCTTCGGCGAGTCACAG-3′; 5′-AAAGGCCTTCTCCTCGTTGT-3′; SEQ ID NO: 245 SEQ ID NO: 246 5′-GGCGGCAAATACTCAGTGAA-3′; 5′-CCTGGGTGTCCTCCTTATCC-3′; SEQ ID NO: 247 SEQ ID NO: 248 GDF15 5′-CGGATACTCACGCCAGAAGT-3′; 5′-AGAGATACGCAGGTGCAGGT-3′; SEQ ID NO: 249 SEQ ID NO: 250 5′-AAGATTCGAACACCGACCTC-3′; 5′-GCACTTCTGGCGTGAGTATC-3′; SEQ ID NO: 251 SEQ ID NO: 252 ARPC1B 5′-CACGCCTGGAACAAGGAC-3′; 5′-ATGCACCTCATGGTTGTTGG-3′; SEQ ID NO: 253 SEQ ID NO: 254 5′-CAGGTGACAGGCATCGACT-3′; 5′-CGCAGGTCACAATACGGTTA-3′; SEQ ID NO: 255 SEQ ID NO: 256 FARP1 5′-TGAGGCCCTGAGAGAGAAGA-3′; 5′-ATTCCGAAACTCCACACGTC-3′; SEQ ID NO: 257 SEQ ID NO: 258 5′-TCAAGGAAATTGAGCAACGA-3′; 5′-TCTGATTTGGGCATTTGAGC-3′; SEQ ID NO: 259 SEQ ID NO: 260 NTRK3 5′-TATGGTCGACGGTCCAAAT-3′; 5′-TCCTCACCACTGATGACAGC-3′; SEQ ID NO: 261 SEQ ID NO: 262 5′-CACTGTGACCCACAAACCAG-3′; 5′-GCAAGTCCAACTGCTATGGA-3′; SEQ ID NO: 263 SEQ ID NO: 264 CSK 5′-TGAGGCCCTGAGAGAGAAGA-3′; 5′-ATTCCGAAACTCCACACGTC-3′; SEQ ID NO: 265 SEQ ID NO: 266 5′-TCTACTCCTTTGGGCGAGTG-3′; 5′-CGTCCTTCAGGGGAATTCTT-3′; SEQ ID NO: 267 SEQ ID NO: 268 CD44 5′-TAAGGACACCCCAAATTCCA-3′; 5′-GCCAAGATGATCAGCCATTC-3′; SEQ ID NO: 269 SEQ ID NO: 270 5′-GCAGTCAACAGTCGAAGAAGG-3′; 5′-AGCTTTTTCTTCTGCCCACA-3′; SEQ ID NO: 271 SEQ ID NO: 272 SNX17 5′-AGCCAGCAAGCAGTGAAGTC-3′; 5′-TCAGGTGACTCAAGCAGTGG-3′; SEQ ID NO: 273 SEQ ID NO: 274 5′-CCGGGAGTCTATGGTCAAAC-3′; 5′-CACGGCACTCAGCTTACTTG-3′; SEQ ID NO: 275 SEQ ID NO: 276 PLAT 5′-TGGAGCAGTCTTCGTTTCG-3′; 5′-CTGGCTCCTCTTCTGAATCG-3′; SEQ ID NO: 277 SEQ ID NO: 278 5′-GCCCGATTCAGAAGAGGAG-3′; 5′-TCATCTCTGCAGATCACTTGG-3′; SEQ ID NO: 279 SEQ ID NO: 280

The following was performed to generate a standard curve for the target of each primer pair. The standard was generated with a defined number of amplicons per volume for each primer pair. In particular, a standard (S7) was designed to contain about 5 million copies of amplicon-containing cDNA in a bacterial expression vector backbone (pJET1.2 obtained from Fermentas) per one microliter volume for each primer pair. From this, six 1:10 dilutions were generated such that seven standards S1 to S7 were obtained ranging from 5 to 5 million copies of amplicon. To obtain fragments of cDNA, total RNA was extracted from the human HaCaT, A431, and A375 cell lines, and the RNA was reverse transcribed into cDNA. Cell line-derived cDNA was used as a template to amplify fragments of cDNA that contained the desired amplicons for the real time-PCR primer pairs. A list of primers used to generate the desired cDNA fragments is listed in Table 3.

TABLE 3 Primer sets for generating cDNA fragments of the indicated genes. Gene Name Forward primer Reverse primer FN1 5′-CCAGCAGAGGCATAAGGTTC-3′; 5′-AGTAGTGCCTTCGGGACTGG-3′; SEQ ID NO: 281 SEQ ID NO: 282 SPP1 5′-AGGCTGATTCTGGAAGTTCTGAGG-3′; 5′-AATCTGGACTGCTTGTGGCTG-3′; SEQ ID NO: 283 SEQ ID NO: 284 COL4A1 5′-GTTGGGCCTCCAGGATTTA-3′; 5′-GCCTGGTAGTCCTGGGAAAC-3′; SEQ ID NO: 285 SEQ ID NO: 286 TNC 5′-TGGATGGATTGTGTTCCTGA-3′; 5′-GCCTGCCTTCAAGATTTCTG-3′; SEQ ID NO: 287 SEQ ID NO: 288 ITGA3 5′-CTGAGACTGTGCTGACCTGTG-3′; 5′-CTCTTCATCTCCGCCTTCTG-3′; SEQ ID NO: 289 SEQ ID NO: 290 LOXL3 5′-GAGACCGCCTACATCGAAGA-3′; 5′-GGTAGCGTTCAAACCTCCTG-3′; SEQ ID NO: 291 SEQ ID NO: 292 AGRN 5′-ACACCGTCCTCAACCTGAAG-3′; 5′-AATGGCCAGTGCCACATAGT-3′; SEQ ID NO: 293 SEQ ID NO: 294 VCAN 5′-GGTGCACTTTGTGAGCAAGA-3′; 5′-TTGGTATGCAGATGGGTTCA-3′; SEQ ID NO: 295 SEQ ID NO: 296 PLOD3 5′-AGCTGTGGTCCAACTTCTGG-3′; 5′-GTGTGGTAACCGGGAAACAG-3′; SEQ ID NO: 297 SEQ ID NO: 298 ITGB1 5′-TTCAGTTTGCTGTGTGTTTGC-3′; 5′-CCACCTTCTGGAGAATCCAA-3′; SEQ ID NO: 299 SEQ ID NO: 300 PTK2 5′-GGCAGTATTGACAGGGAGGA-3′; 5′-TACTCTTGCTGGAGGCTGGT-3′; SEQ ID NO: 301 SEQ ID NO: 302 CTGF 5′-GCCTATTCTGTCACTTCGGCTC-3′; 5′-GCAGGCACAGGTCTTGATGAAC-3′; SEQ ID NO: 303 SEQ ID NO: 304 PLOD1 5′-GACCTCTGGGAGGTGTTCAG-3′; 5′-TTAGGGATCGACGAAGGAGA-3′; SEQ ID NO: 305 SEQ ID NO: 306 LAMC1 5′-ATTCCTGCCATCAACCAGAC-3′; 5′-CCTGCTTCTTGGCTTCATTC-3′; SEQ ID NO: 307 SEQ ID NO: 308 THBS1 5′-CAAAGGGACATCCCAAAATG-3′; 5′-GAGTCAGCCATGATTTTCTTCC-3′; SEQ ID NO: 309 SEQ ID NO: 310 LOXL2 5′-TACCCCGAGTACTTCCAGCA-3′; 5′-GATCTGCTTCCAGGTCTTGC-3′; SEQ ID NO: 311 SEQ ID NO: 312 IL6 5′-CACACAGACAGCCACTCACC-3′; 5′-CAGGGGTGGTTATTGCATCT-3′; SEQ ID NO: 313 SEQ ID NO: 314 LOXL1 5′-CAGACCCCAACTATGTGCAA-3′; 5′-CGCATTGTAGGTGTCATAGCA-3′; SEQ ID NO: 315 SEQ ID NO: 316 IL8 5′-CTCTCTTGGCAGCCTTCCT-3′; 5′-TGAATTCTCAGCCCTCTTCAA-3′; SEQ ID NO: 317 SEQ ID NO: 318 CYR61 5′-TCGCCTTAGTCGTCACCCTT-3′; 5′-TGTTTCTCGTCAACTCCACCTCG-3′; SEQ ID NO: 319 SEQ ID NO: 320 ITGAV 5′-CTGATTTCATCGGGGTTGTC-3′; 5′-TGCCTTGCTGAATGAACTTG-3′; SEQ ID NO: 321 SEQ ID NO: 322 YAP 5′-CCAGTGAAACAGCCACCAC-3′; 5′-CTCCTTCCAGTGTTCCAAGG-3′; SEQ ID NO: 323 SEQ ID NO: 324 BGN 5′-GGACTCTGTCACACCCACCT-3′; 5′-CAGGGTCTCAGGGAGGTCTT-3′; SEQ ID NO: 325 SEQ ID NO: 326 LAMB1 5′-TGCCAGAGCTGAGATGTTGTT-3′; 5′-TGTAGCATTTCGGCTTTCCT-3′; SEQ ID NO: 327 SEQ ID NO: 328 ITGB3 5′-GGCAAGTACTGCGAGTGTGA-3′; 5′-ATTCTTTTCGGTCGTGGATG-3′; SEQ ID NO: 329 SEQ ID NO: 330 CXCL1 5′-CACTGCTGCTCCTGCTCCT-3′; 5′-TGTTCAGCATCTTTTCGATGA-3′; SEQ ID NO: 331 SEQ ID NO: 332 THBS2 5′-TGACAATGACAACATCCCAGA-3′; 5′-TGAGTCTGCCATGACCTGTT-3′; SEQ ID NO: 333 SEQ ID NO: 334 COL18A1 5′-CCCTGCTCTACACAGAACCAG-3′; 5′-ACACCTGGCTCCCCTTTCT-3′; SEQ ID NO: 335 SEQ ID NO: 336 SPARC 5′-GCCTGGATCTTCTTTCTCCTTTGC-3′; 5′-CATCCAGGGCGATGTACTTGTC-3′; SEQ ID NO: 337 SEQ ID NO: 338 TP53 5′-CCCCCTCTGAGTCAGGAAAC-3′; 5′-TCATGTGCTGTGACTGCTTG-3′; SEQ ID NO: 339 SEQ ID NO: 340 PLOD2 5′-TGGACCCACCAAGATTCTCCTG-3′; 5′-GACCACAGCTTTCCATGACGAG-3′; SEQ ID NO: 341 SEQ ID NO: 342 CCL2 5′-TCTGTGCCTGCTGCTCATAG-3′; 5′-GAGTTTGGGTTTGCTTGTCC-3′; SEQ ID NO: 343 SEQ ID NO: 344 FBLN2 5′-CGAGAAGTGCCCAGGAAG-3′; 5′-AGTGAGAAGCCAGGAAAGCA-3′; SEQ ID NO: 345 SEQ ID NO: 346 LAMA1 5′-TGGAAATATCACCCACAGCA-3′; 5′-AGGCATTTTTGCTTCACACC-3′; SEQ ID NO: 347 SEQ ID NO: 348 THBS4 5′-GCTCCAGCTTCTACGTGGTC-3′; 5′-TTAATTATCGAAGCGGTCGAA-3′; SEQ ID NO: 349 SEQ ID NO: 350 COL1A1 5′-AGCCAGCAGATCGAGAACAT-3′; 5′-CCTTCTTGAGGTTGCCAGTC-3′; SEQ ID NO: 351 SEQ ID NO: 352 ITGA5 5′-CACCAATCACCCCATTAACC-3′; 5′-GCTTGAGCTGAGCTTTTTCC-3′; SEQ ID NO: 353 SEQ ID NO: 354 TAZ 5′-CCAGGTGCTGGAAAAAGAAG-3′; 5′-GAGCTGCTCTGCCTGAGTCT-3′; SEQ ID NO: 355 SEQ ID NO: 356 POSTN 5′-GCAGACACACCTGTTGGAAA-3′; 5′-GAACGACCTTCCCTTAATCG-3′; SEQ ID NO: 357 SEQ ID NO: 358 LOX 5′-CCTACTACATCCAGGCGTCCAC-3′; 5′-ATGCAAATCGCCTGTGGTAGC-3′; SEQ ID NO: 359 SEQ ID NO: 360 CSRC 5′-CTGTTCGGAGGCTTCAACTC-3′; 5′-AGGGATCTCCCAGGCATC-3′; SEQ ID NO: 361 SEQ ID NO: 362 LAMA3 5′-TACCTGGGATCACCTCCATC-3′; 5′-ACAGGGATCCTCAGTGTCGT-3′; SEQ ID NO: 363 SEQ ID NO: 364 CDKN1A 5′-CGGGATGAGTTGGGAGGAG-3′; 5′-TTAGGGCTTCCTCTTGGAGA-3′; SEQ ID NO: 365 SEQ ID NO: 366 CDKN2A-004 5′-ATGGTGCGCAGGTTCTTG-3′; 5′-ACCAGCGTGTCCAGGAAG-3′; 2A-201 SEQ ID NO: 367 SEQ ID NO: 368 CDKN2A-001 5′-GAGCAGCATGGAGCCTTC-3′; 5′-GCATGGTTACTGCCTCTGGT-3′; 2A-202 SEQ ID NO: 369 SEQ ID NO: 370 ITGA2 5′-CAAACAGACAAGGCTGGTGA-3′; 5′-TCAATCTCATCTGGATTTTTGG-3′; SEQ ID NO: 371 SEQ ID NO: 372 LAMC2 5′-CTGCAGGTGGACAACAGAAA-3′; 5′-CATCAGCCAGAATCCCATCT-3′; SEQ ID NO: 373 SEQ ID NO: 374 PCOLCE2 5′-GTCCCCAGAGAGACCTGTTT-3′; 5′-AGACACAATTGGCGCAGGT-3′; SEQ ID NO: 375 SEQ ID NO: 376 LOXL4 5′-AAGACTGGACGCGATAGCTG-3′; 5′-GGTTGTTCCTGAGACGCTGT-3′; SEQ ID NO: 377 SEQ ID NO: 378 PCOLCE 5′-TACACCAGACCCGTGTTCCT-3′; 5′-TCCAGGTCAAACTTCTCGAAGG-3′; SEQ ID NO: 379 SEQ ID NO: 380 LAMB3 5′-CTTCAATGCCCAGCTCCA-3′; 5′-TTCCCAACCACATCTTCCAC-3′; SEQ ID NO: 381 SEQ ID NO: 382 CSF2 5′-CTGCTGCTCTTGGGCACT-3′; 5′-CAGCAGTCAAAGGGGATGAC-3′; SEQ ID NO: 383 SEQ ID NO: 384 ACTB 5′-AGGATTCCTATGTGGGCGACG-3′; 5′-TCAGGCAGCTCGTAGCTCTTC-3′; SEQ ID NO: 385 SEQ ID NO: 386 RPLP0 5′-GGAATGTGGGCTTTGTGTTCACC-3′; 5′-AGGCCAGGACTCGTTTGTACC-3′; SEQ ID NO: 387 SEQ ID NO: 388 RLP8 5′-ACATCAAGGGCATCGTCAAGG-3′; 5′-TCTCTTTCTCCTGCACAGTCTTGG-3′; SEQ ID NO: 389 SEQ ID NO: 390 B2M 5′-TGCTCGCGCTACTCTCTCTTTC-3′; 5′-TCACATGGTTCACACGGCAG-3′; SEQ ID NO: 391 SEQ ID NO: 392 K10 5′-TGGCCTTCTCTCTGGAAATG-3′; 5′-TCATTTCCTCCTCGTGGTTC-3′; SEQ ID NO: 393 SEQ ID NO: 394 K14 5′-AGGTGACCATGCAGAACCTC-3′; 5′-CCTCGTGGTTCTTCTTCAGG-3′; SEQ ID NO: 395 SEQ ID NO: 396 MITF 5′-GAAATCTTGGGCTTGATGGA-3′; 5′-CCGAGGTTGTTGTTGAAGGT-3′; SEQ ID NO: 397 SEQ ID NO: 398 TYR 5′-CCATGGATAAAGCTGCCAAT-3′; 5′-GACACAGCAAGCTCACAAGC-3′; SEQ ID NO: 399 SEQ ID NO: 400 MLANA 5′-CACTCTTACACCACGGCTGA-3′; 5′-CATAAGCAGGTGGAGCATTG-3′; SEQ ID NO: 401 SEQ ID NO: 402 PMEL 5′-TTGTCCAGGGTATTGAAAGTGC-3′; 5′-GACAAGAGCAGAAGATGCGGG-3′; SEQ ID NO: 403 SEQ ID NO: 404 NES 5′-GCGTTGGAACAGAGGTTGGAG-3′; 5′-CAGGTGTCTCAAGGGTAGCAGG-3′; SEQ ID NO: 405 SEQ ID NO: 406 L1CAM 5′-CTTCCCTTTCGCCACAGTATG-3′; 5′-CCTCCTTCTCCTTCTTGCCACT-3′; SEQ ID NO: 407 SEQ ID NO: 408 GDF15 5′-AATGGCTCTCAGATGCTCCTGG-3′; 5′-GATTCTGCCAGCAGTTGGTCC-3′; SEQ ID NO: 409 SEQ ID NO: 410 ARPC1B 5′-ACCACAGCTTCCTGGTGGAG-3′; 5′-GAGCGGATGGGCTTCTTGATG-3′; SEQ ID NO: 411 SEQ ID NO: 412 FARP1 5′-AACGTGACCTTGTCTCCCAAC-3′; 5′-GCATGACATCGCCGATTCTT-3′; SEQ ID NO: 413 SEQ ID NO: 414 NTRK3 5′-TTCAACAAGCCCACCCACTAC-3′; 5′-GTTCTCAATGACAGGGATGCG-3′; SEQ ID NO: 415 SEQ ID NO: 416 CSK 5′-CATGGAATACCTGGAGGGCAAC-3′; 5′-CAGGTGCCAGCAGTTCTTCAT-3′; SEQ ID NO: 417 SEQ ID NO: 418 CD44 5′-TCTCAGAGCTTCTCTACATCAC-3′; 5′-CTGACGACTCCTTGTTCACCA-3′; SEQ ID NO: 419 SEQ ID NO: 420 SNX17 5′-TCACCTCCTCTGTACCATTGC-3′; 5′-CTCATCTCCAATGCCCTCGA-3′; SEQ ID NO: 421 SEQ ID NO: 422 PLAT 5′-TGCAATGAAGAGAGGGCTCTG-3′; 5′-CGTGGCCCTGGTATCTATTTCA-3′; SEQ ID NO: 432 SEQ ID NO: 424

The PCR reactions were performed using a high-fidelity polymerase (product name: “Phusion,” obtained from New England Biolabs). PCR amplification products were checked for correct size and subsequently gel purified using the Qiagen Gel Extraction kit. Purified PCR fragments were subcloned into the bacterial expression vector pJET1.2 using a commercially available kit (Fermentas). The subcloned fragments were subsequently checked by restriction digest and DNA sequencing. Bacterial clones harboring the pJET1.2 expression vector with the correct PCR insert (containing the desired amplicon for real time PCR primer pairs) were frozen and stored at −80° C. This was done to regenerate the same real time PCR standards over time.

Bacteria harboring the pJET1.2 expression vector with PCR inserts were cultured to generate sufficient amounts of vector. A small aliquot of the total retrieved expression vector with insert was linearized using the PvuI-HF restriction enzyme (from New England Biolabs). The digest was then purified using the Qiagen PCR purification kit. Linearized cDNA was diluted to a concentration of 20 ng/μL. One μL of each of a total of 71 linearized cDNA fragments (each at a 20 ng/μL concentration) were mixed and brought to a final volume of 1 mL to obtain standard S7.

Standard S7 was then diluted six times at a 1:10 ratio to obtained standards S1 to S6. Dilution was performed using ultrapure water obtained from Promega (Cat. No. P1193).

The following was performed to generate cDNA from FFPE samples. FFPE blocks were cut at 20 μm sections using a standard Leica microtome. For large pieces of tissue, 2×20 μm full sections were used for RNA retrieval. For smaller tissues, up to 5×20 μm sections were combined for RNA retrieval. RNA extraction was performed using the Qiagen RNA FFPE retrieval kit and a Qiagen QiaCube extraction robot. 0.5 to 1 μg of RNA with a 260/280 ratio of greater than 1.8 were transcribed into cDNA using the BioRad iScript cDNA Synthesis kit. All biospecimens were annotated with clinical data from Mayo Clinic databases. H&E stained sections were obtained for each block analyzed and digitalized using a high-resolution slide scanner.

Fluidigm RT-PCR was performed using a 96×96 format for high throughput analysis (i.e., 96 cDNAs were analyzed for 96 markers; 9216 data points). The primer pairs and cDNAs were prepared in a 96 well format. Standard curves were calculated for each primer pair. Copy numbers per 100,000 housekeeping genes were calculated for each primer pair and averaged per gene. This was initially done for cDNAs derived from FFPE-embedded skin. To correct for epidermal cell-derived cross-contamination, background signal per one copy of K14 (a basal keratinocyte marker) was calculated from FFPE-embedded normal skin samples for each primer pair and averaged. Experimental samples were then normalized first to 100,000 housekeeping genes and then background-corrected for epidermal cross-contamination based on K14 copy number. In particular, the keratinocyte correction factor used for each gene is set forth in Table E under the column titled “AVG per copy K14.”

The study design (Example 1) involved a comparison of the expression profile of “true” benign pigmented skin lesions (nevi, n=73) with “true” malignant melanomas of the skin. The latter comprised i) primary skin melanomas that were documented to metastasize, either to regional lymph nodes, to other areas of skin (in-transit), or to other organs; and ii) in-transit or comparison of nevi to in-transit melanoma metastases (n=54).

Tables C and D summarize the comparisons of the gene expressions between the 73 benign and 54 metastatic. Table A compares the ranked values using the Wilcoxon rank sum test, and Table E compares the dichotomized values (zero vs. >0) using the chi-square test.

A recursive partitioning approach was used to identify cut-points for the genes that would discriminate between these two groups. After partitioning the data at a cut-point of 45 for FN1, no further additional splits in the data based on the other genes were identified by this method.

Using a cutoff of 45 for FN1, the sensitivity was 92.6%, and the specificity was 98.6%. These results are provided in Tables 4 and 5 along with the next possible cutoff for FN1 at 124.

TABLE 4 Frequency Percent Row Pct Col Pct Malignant Benign Total FN1 < 45 4 72 76 3.15 56.69 59.84 5.26 94.74 7.41 98.63 FN1 ≥ 45 50 1 51 39.37 0.79 40.16 98.04 1.96 92.59 1.37 Total 54 73 127 42.52 57.48 100.00

TABLE 5 Frequency Percent Row Pct Col Pct Malignant Benign Total FN1 < 124 8 73 81 6.30 57.48 63.78 9.88 90.12 14.81 100.00 FN1 ≥ 124 46 0 46 36.22 0.00 36.22 100.00 0.00 85.19 0.00 Total 54 73 127 42.52 57.48 100.00

The ability to further discriminate between the groups was assessed by considering SPP1 or ITGB3 in addition to FN1.

Benign Vs. Malignant—Option 1 Using FN1 and SPP1 (FIG. 5)

The results are set forth in Table 6.

TABLE 6 RULE for FIG. 5 Malignant Benign FN1 < 45 and SPP1 = 0 2 72 FN1 ≥ 45 52 1 or (FN1 < 45 and SPP1 > 0) Total 54 73 Benign Vs. Malignant—Option 2 Using FN1 and ITGB3 (FIG. 6)

The results are set forth in Table 7.

TABLE 7 RULE for FIG. 6 Malignant Benign FN1 < 45 and ITGB3 = 0 3 72 FN1 ≥ 45 51 1 or (FN1 < 45 and ITGB3 > 0) Total 54 73

If all three genes are included, the rule was as follows:

-   -   FN1<45 and SPP1=0 and ITGB3=0 denotes a negative test         -   vs.     -   all other combinations denotes a positive test.

This rule resulted in a specificity of 72/73 (98.6%), and a sensitivity of 53/54 (98.2%) (Table 8). Compared to a rule using FN1 alone, the specificity stayed the same but the sensitivity increased from 92.6% to 98.2% using this new rule.

TABLE 8 FN1 SPP1 ITGB3 malignant Frequency <45 Zero Zero No 72 <45 Zero Zero Yes 1 False Neg ID MM150 (case added from the Breslow file) ≥45 Zero Zero No 1 False Pos ID N29 ≥45 Zero Zero Yes 9 ≥45 Zero >0 Yes 1 ≥45 >0 Zero Yes 18 ≥45 >0 >0 Yes 22 <45 Zero >0 Yes 1 <45 >0 Zero Yes 2

The rule was evaluated using 25 additional malignant patients who did not have mets (from the “Breslow” file). For 19 of these 25 patients, the rule was “negative” (Table 9).

TABLE 9 FN1 SPP1 ITGB3 Frequency <45 Zero Zero 19 <45 >0 Zero 1 ≥45 Zero Zero 2 ≥45 >0 Zero 3 <45 1

The rule also was evaluated using 33 thin melanomas (Table 10). For 25 of these 33 patients, the rule was “negative.”

TABLE 10 FN1 SPP1 ITGB3 Frequency <45 Zero Zero 25 <45 Zero >0 1 ≥45 Zero Zero 5 ≥45 >0 Zero 2

TABLE C Comparison of gene expression between benign and malignant Benign (N = 73) Malignant (N = 54) p value CXCL1_AVG_NORM <0.0001 N 73 54 Mean (SD) 4.8 (18.4) 20.0 (26.1) Median   0.0   10.3 Q1, Q3 0.0, 0.0  0.3, 31.1 Range (0.0-141.7) (0.0-120.4) CSF2_AVG_NORM 0.0482 N 73 54 Mean (SD) 10.5 (44.1) 4.3 (8.4) Median   2.5   1.0 Q1, Q3 0.6, 7.0 0.0, 4.0 Range (0.0-375.0)  (0.0-41.0) CCL2_AVG_NORM <0.0001 N 73 54 Mean (SD) 37.0 (99.4) 244.2 (360.9) Median   0.0  112.8 Q1, Q3 0.0, 9.1  7.2, 342.2 Range (0.0-572.0)  (0.0-1777.1) IL8_AVG_NORM <0.0001 N 73 54 Mean (SD) 125.5 (671.3)  53.2 (160.8) Median   0.0   13.0 Q1, Q3 0.0, 0.0  2.1, 52.5 Range  (0.0-5058.7)  (0.0-1171.7) IL6_AVG_NORM <0.0001 N 73 54 Mean (SD)  9.9 (69.1) 21.6 (35.0) Median   0.0   8.8 Q1, Q3 0.0, 0.0  0.3, 25.2 Range (0.0-589.1) (0.0-152.3) ITGA5_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  9.8 (26.8) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 7.0 Range (0.0-0.0)  (0.0-168.0) ITGA3_AVG_NORM <0.0001 N 73 54 Mean (SD)  3.2 (27.5) 168.2 (313.4) Median   0.0   50.2 Q1, Q3 0.0, 0.0  2.0, 160.5 Range (0.0-235.4)  (0.0-1506.0) ITGA2_AVG_NORM 0.0007 N 73 54 Mean (SD) 0.0 (0.0)  2.6 (10.0) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0 Range (0.0-0.0)  (0.0-69.7)  ITGAV_AVG_NORM <0.0001 N 73 54 Mean (SD)  3.3 (23.9) 22.0 (32.9) Median   0.0   8.0 Q1, Q3 0.0, 0.0  0.0, 31.0 Range (0.0-199.9) (0.0-176.8) ITGB3_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 43.6 (90.3) Median   0.0   0.0 Q1, Q3 0.0, 0.0  0.0, 52.5 Range (0.0-0.0)  (0.0-495.3) ITGB1_AVG_NORM <0.0001 N 73 54 Mean (SD) 29.9 (95.1) 616.2 (742.2) Median   0.0  400.2 Q1, Q3 0.0, 0.0  84.7, 869.0 Range (0.0-487.9)  (0.0-3877.9) FN1_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.9 (15.6) 1570.9 (1949.8) Median   0.0  898.4 Q1, Q3 0.0, 0.0  299.5, 2186.1 Range (0.0-123.2)  (0.0-11073.5) THBS1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  85.1 (136.1) Median   0.0   16.8 Q1, Q3 0.0, 0.0  0.0, 153.8 Range (0.0-0.0)  (0.0-786.2) THBS2_AVG_NORM <0.0001 N 73 54 Mean (SD)  25.9 (113.4) 280.0 (513.5) Median   0.0   44.1 Q1, Q3 0.0, 0.0  0.0, 340.1 Range (0.0-729.2)  (0.0-3030.5) THBS4_AVG_NORM <0.0001 N 73 54 Mean (SD)  38.5 (151.2) 228.2 (663.7) Median   0.0   22.5 Q1, Q3 0.0, 0.0  0.0, 97.9 Range  (0.0-1130.3)  (0.0-3977.7) VCAN_AVG_NORM <0.0001 N 73 54 Mean (SD)  3.0 (21.7) 202.4 (262.8) Median   0.0  103.4 Q1, Q3 0.0, 0.0  0.0, 283.5 Range (0.0-181.3)  (0.0-1113.2) BGAN_AVG_NORM <0.0001 N 73 54 Mean (SD)  69.3 (121.0) 422.4 (573.1) Median   0.0  248.5 Q1, Q3  0.0, 97.9 113.5, 462.9 Range (0.0-496.3)  (0.0-3348.1) SPP1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 1490.2 (3397.4) Median   0.0  338.1 Q1, Q3 0.0, 0.0   4.9, 1577.7 Range (0.0-0.0)   (0.0-22427.0) TNC_AVG_NORM <0.0001 N 73 54 Mean (SD)  66.4 (240.1) 800.1 (808.7) Median   0.0  495.8 Q1, Q3 0.0, 0.0  174.5, 1322.9 Range  (0.0-1393.3)  (0.0-3162.2) SPARC_AVG_NORM <0.0001 N 73 54 Mean (SD)  843.7 (2222.8) 3208.4 (3182.6) Median   0.0 2895.8  Q1, Q3 0.0, 0.0  407.2, 5216.3 Range  (0.0-11175.6)  (0.0-13631.9) AGRN_AVG_NORM <0.0001 N 73 54 Mean (SD)  4.7 (18.1) 51.2 (53.8) Median   0.0   42.1 Q1, Q3 0.0, 0.0 10.7, 69.7 Range (0.0-121.7) (0.0-242.0) CTGF_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.4 (3.6)  90.9 (231.6) Median   0.0   22.1 Q1, Q3 0.0, 0.0  0.0, 125.9 Range (0.0-30.6)   (0.0-1631.4) CYR61_AVG_NORM <0.0001 N 73 54 Mean (SD)  4.8 (13.0) 27.2 (39.2) Median   0.0   18.7 Q1, Q3 0.0, 0.0  4.9, 32.2 Range (0.0-70.4)  (0.0-267.2) LAMA3_AVG_NORM 0.0004 N 73 54 Mean (SD) 1.1 (9.0) 1.2 (2.9) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0 Range (0.0-76.8)  (0.0-11.3)  LAMC1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  70.6 (159.4) Median   0.0   28.4 Q1, Q3 0.0, 0.0  0.0, 99.3 Range (0.0-0.0)   (0.0-1136.2) LAMB1_AVG_NORM <0.0001 N 73 54 Mean (SD)  9.2 (38.4) 221.1 (354.3) Median   0.0   73.1 Q1, Q3 0.0, 0.0  0.0, 339.8 Range (0.0-248.8)  (0.0-1877.6) LAMA1_AVG_NORM <0.0001 N 73 54 Mean (SD)  5.7 (14.5)  65.4 (149.0) Median  <0.0   10.6 Q1, Q3 0.0, 0.0  0.0, 49.0 Range (0.0-76.5)  (0.0-754.3) LAMC2_AVG_NORM 0.0003 N 73 54 Mean (SD) 0.0 (0.0)  4.0 (15.3) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0 Range (0.0-0.0)  (0.0-91.1)  LAMB3_AVG_NORM 0.1473 N 73 54 Mean (SD) 33.5 (60.3) 32.2 (54.5) Median   0.0   12.1 Q1, Q3  0.0, 44.6  0.0, 37.0 Range (0.0-323.9) (0.0-246.0) COL1A1_AVG_NORM <0.0001 N 73 54 Mean (SD) 1534.4 (4365.3) 4191.6 (5865.9) Median   0.0 1704.4  Q1, Q3 0.0, 0.0   0.0, 6850.9 Range  (0.0-22510.2)  (0.0-31867.0) COL4A1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 211.8 (344.1) Median   0.0  118.4 Q1, Q3 0.0, 0.0  2.3, 261.2 Range (0.0-0.0)   (0.0-1774.4) COL18A1_AVG_NORM <0.0001 N 73 54 Mean (SD)  94.2 (783.4) 22.8 (38.8) Median   0.0   4.1 Q1, Q3 0.0, 0.0  0.0, 34.4 Range  (0.0-6695.7) (0.0-208.8) LOX_AVG_NORM 0.0003 N 73 54 Mean (SD)  37.7 (132.8)  65.0 (113.9) Median   0.0   3.5 Q1, Q3 0.0, 0.0  0.0, 58.0 Range (0.0-991.2) (0.0-443.3) LOXL1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.8 (7.1) 39.6 (60.3) Median   0.0   18.5 Q1, Q3 0.0, 0.0  0.0, 65.0 Range (0.0-60.4)  (0.0-349.0) LOXL2_AVG_NORM <0.0001 N 73 54 Mean (SD)  43.3 (356.8)  68.5 (129.9) Median   0.0   22.1 Q1, Q3 0.0, 0.0  0.0, 89.1 Range  (0.0-3048.4) (0.0-821.4) LOXL3_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.2 (12.3) 28.4 (71.1) Median   0.0   9.2 Q1, Q3 0.0, 0.0  2.5, 29.4 Range (0.0-89.7)  (0.0-507.5) LOXL4_AVG_NORM 0.0010 N 73 54 Mean (SD) 33.8 (91.0) 129.1 (300.4) Median   0.0   9.1 Q1, Q3  0.0, 10.2  0.0, 67.0 Range (0.0-529.2)  (0.0-1230.0) PLOD1_AVG_NORM <0.0001 N 73 54 Mean (SD)  33.7 (116.5) 420.3 (532.2) Median   0.0  242.3 Q1, Q3 0.0, 0.0  90.2, 659.3 Range (0.0-878.2)  (0.0-3336.8) PLOD2_AVG_NORM <0.0001 N 73 54 Mean (SD)  44.5 (151.7)  314.8 (1284.4) Median   0.0   53.7 Q1, Q3 0.0, 0.0  2.3, 103.3 Range  (0.0-1124.0)  (0.0-9110.5) PLOD3_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.7 (11.9) 68.0 (81.2) Median   0.0   38.3 Q1, Q3 0.0, 0.0  4.2, 101.9 Range (0.0-87.4)  (0.0-330.2) PCOLCE2_AVG_NORM 0.0010 N 73 54 Mean (SD)  7.7 (25.8)  6.4 (14.9) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 3.1 Range (0.0-104.8) (0.0-68.4)  PCOLCE_AVG_NORM 0.0232 N 73 54 Mean (SD)  92.1 (159.7) 170.4 (339.4) Median   0.0   40.9 Q1, Q3  0.0, 122.2  0.0, 175.1 Range (0.0-699.2)  (0.0-1945.2) PTK2_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.8 (14.4) 76.6 (81.8) Median   0.0   70.0 Q1, Q3 0.0, 0.0  0.0, 127.7 Range (0.0-116.5) (0.0-323.3) CSRC_AVG_NORM 0.0001 N 73 54 Mean (SD) 19.0 (40.9) 45.1 (65.9) Median   0.3   19.6 Q1, Q3  0.0, 24.8  4.2, 46.6 Range (0.0-266.6) (0.0-290.2) CDKN1A_AVG_NORM 0.0005 N 73 54 Mean (SD)  78.5 (150.9) 181.0 (271.7) Median   0.0   84.2 Q1, Q3  0.0, 118.9  0.0, 253.3 Range (0.0-788.2)  (0.0-1083.2) CDKN2A_AVG_NORM 0.0002 N 73 54 Mean (SD)  6.1 (19.6)  9.7 (25.8) Median   0.0   1.0 Q1, Q3 0.0, 0.0 0.0, 6.9 Range (0.0-113.2) (0.0-175.1) TP53_AVG_NORM <0.0001 N 73 54 Mean (SD) 40.6 (98.6) 231.2 (289.8) Median   0.0  166.9 Q1, Q3 0.0, 0.0  0.0, 359.9 Range (0.0-410.8)  (0.0-1722.4) YAP_AVG_NORM <0.0001 N 73 54 Mean (SD)  7.8 (36.6) 112.4 (161.4) Median   0.0   63.1 Q1, Q3 0.0, 0.0  0.0, 173.5 Range (0.0-246.3) (0.0-769.0) TAZ_AVG_NORM <0.0001 N 73 54 Mean (SD) 12.2 (27.9) 32.8 (44.3) Median   0.0   15.0 Q1, Q3 0.0, 0.7  0.0, 49.0 Range (0.0-122.7) (0.0-186.4) MITF_AVG_NORM <0.0001 N 73 54 Mean (SD) 251.0 (399.5) 569.8 (494.8) Median   45.5  467.3 Q1, Q3  0.0, 331.5 184.9, 777.8 Range  (0.0-2143.3)  (0.0-2200.0) MLANA_AVG_NORM 0.1823 N 73 54 Mean (SD) 3596.0 (3671.3) 4865.4 (4966.1) Median 2446.8  2803.5  Q1, Q3  950.9, 5019.4 1210.7, 6773.0 Range  (14.0-17180.3)  (62.8-19672.1) TYR_AVG_NORM 0.0040 N 73 54 Mean (SD) 349.7 (301.8) 839.8 (996.3) Median  254.3  515.1 Q1, Q3 119.5, 527.5  161.0, 1244.9 Range  (0.0-1169.8)  (2.0-5500.0) POSTN_AVG_NORM 0.0001 N 73 54 Mean (SD) 1138.7 (2155.7) 1933.9 (2318.1) Median  191.6  1252.0 Q1, Q3   0.0, 1449.9  397.4, 2457.4 Range  (0.0-11078.1)  (0.0-11193.2) FBLN2_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.1 (17.3) 26.5 (42.2) Median   0.0   0.0 Q1, Q3 0.0, 0.0  0.0, 48.8 Range (0.0-148.2) (0.0-150.9)

TABLE D Comparison of gene expression between benign and malignant Benign Malignant (N = 73) (N = 54) p value CXCL1_AVG_NORM01 <0.0001 Zero 58 (79.5%) 12 (22.2%) >0 15 (20.5%) 42 (77.8%) CSF2_AVG_NORM01 0.0398 Zero 15 (20.5%) 20 (37.0%) >0 58 (79.5%) 34 (63.0%) CCL2_AVG_NORM01 <0.0001 Zero 53 (72.6%) 12 (22.2%) >0 20 (27.4%) 42 (77.8%) IL8_AVG_NORM01 <0.0001 Zero 63 (86.3%) 10 (18.5%) >0 10 (13.7%) 44 (81.5%) IL6_AVG_NORM01 <0.0001 Zero 65 (89.0%) 13 (24.1%) >0 8 (11.0%) 41 (75.9%) ITGA5_AVG_NORM01 <0.0001 Zero 73 (100.0%) 38 (70.4%) >0 0 (0.0%) 16 (29.6%) ITGA3_AVG_NORM01 <0.0001 Zero 72 (98.6%) 13 (24.1%) >0 1 (1.4%) 41 (75.9%) ITGA2_AVG_NORM01 0.0007 Zero 73 (100.0%) 46 (85.2%) >0 0 (0.0%) 8 (14.8%) ITGAV_AVG_NORM01 <0.0001 Zero 71 (97.3%) 24 (44.4%) >0 2 (2.7%) 30 (55.6%) ITGB3_AVG_NORM01 <0.0001 Zero 73 (100.0%) 30 (55.6%) >0 0 (0.0%) 24 (44.4%) ITGB1_AVG_NORM01 <0.0001 Zero 64 (87.7%) 11 (20.4%) >0 9 (12.3%) 43 (79.6%) FN1_AVG_NORM01 <0.0001 Zero 69 (94.5%) 2 (3.7%) >0 4 (5.5%) 52 (96.3%) THBS1_AVG_NORM01 <0.0001 Zero 73 (100.0%) 24 (44.4%) >0 0 (0.0%) 30 (55.6%) THBS2_AVG_NORM01 <0.0001 Zero 67 (91.8%) 23 (42.6%) >0 6 (8.2%) 31 (57.4%) THBS4_AVG_NORM01 <0.0001 Zero 58 (79.5%) 15 (27.8%) >0 15 (20.5%) 39 (72.2%) VCAN_AVG_NORM01 <0.0001 Zero 71 (97.3%) 16 (29.6%) >0 2 (2.7%) 38 (70.4%) BGAN_AVG_NORM01 <0.0001 Zero 42 (57.5%) 7 (13.0%) >0 31 (42.5%) 47 (87.0%) SPP1_AVG_NORM01 <0.0001 Zero 73 (100.0%) 12 (22.2%) >0 0 (0.0%) 42 (77.8%) TNC_AVG_NORM01 <0.0001 Zero 60 (82.2%) 3 (5.6%) >0 13 (17.8%) 51 (94.4%) SPARC_AVG_NORM01 <0.0001 Zero 57 (78.1%) 13 (24.1%) >0 16 (21.9%) 41 (75.9%) AGRN_AVG_NORM01 <0.0001 Zero 59 (80.8%) 5 (9.3%) >0 14 (19.2%) 49 (90.7%) CTGF_AVG_NORM01 <0.0001 Zero 72 (98.6%) 21 (38.9%) >0 1 (1.4%) 33 (61.1%) CYR61_AVG_NORM01 <0.0001 Zero 56 (76.7%) 9 (16.7%) >0 17 (23.3%) 45 (83.3%) LAMA3_AVG_NORM01 0.0003 Zero 72 (98.6%) 43 (79.6%) >0 1 (1.4%) 11 (20.4%) LAMC1_AVG_NORM01 <0.0001 Zero 73 (100.0%) 24 (44.4%) >0 0 (0.0%) 30 (55.6%) LAMB1_AVG_NORM01 <0.0001 Zero 66 (90.4%) 22 (40.7%) >0 7 (9.6%) 32 (59.3%) LAMA1_AVG_NORM01 <0.0001 Zero 57 (78.1%) 16 (29.6%) >0 16 (21.9%) 38 (70.4%) LAMC2_AVG_NORM01 0.0003 Zero 73 (100.0%) 45 (83.3%) >0 0 (0.0%) 9 (16.7%) LAMB3_AVG_NORM01 0.0061 Zero 45 (61.6%) 20 (37.0%) >0 28 (38.4%) 34 (63.0%) COL1A1_AVG_NORM01 <0.0001 Zero 60 (82.2%) 17 (31.5%) >0 13 (17.8%) 37 (68.5%) COL4A1_AVG_NORM01 <0.0001 Zero 73 (100.0%) 13 (24.1%) >0 0 (0.0%) 41 (75.9%) COL18A1_AVG_NORM01 <0.0001 Zero 64 (87.7%) 18 (33.3%) >0 9 (12.3%) 36 (66.7%) LOX_AVG_NORM01 <0.0001 Zero 60 (82.2%) 26 (48.1%) >0 13 (17.8%) 28 (51.9%) LOXL1_AVG_NORM01 <0.0001 Zero 72 (98.6%) 23 (42.6%) >0 1 (1.4%) 31 (57.4%) LOXL2_AVG_NORM01 <0.0001 Zero 70 (95.9%) 19 (35.2%) >0 3 (4.1%) 35 (64.8%) LOXL3_AVG_NORM01 <0.0001 Zero 69 (94.5%) 10 (18.5%) >0 4 (5.5%) 44 (81.5%) LOXL4_AVG_NORM01 0.0006 Zero 53 (72.6%) 23 (42.6%) >0 20 (27.4%) 31 (57.4%) PLOD1_AVG_NORM01 <0.0001 Zero 59 (80.8%) 12 (22.2%) >0 14 (19.2%) 42 (77.8%) PLOD2_AVG_NORM01 <0.0001 Zero 59 (80.8%) 10 (18.5%) >0 14 (19.2%) 44 (81.5%) PLOD3_AVG_NORM01 <0.0001 Zero 66 (90.4%) 11 (20.4%) >0 7 (9.6%) 43 (79.6%) PCOLCE2_AVG_NORM01 0.0002 Zero 66 (90.4%) 34 (63.0%) >0 7 (9.6%) 20 (37.0%) PCOLCE_AVG_NORM01 0.0036 Zero 42 (57.5%) 17 (31.5%) >0 31 (42.5%) 37 (68.5%) PTK2_AVG_NORM01 <0.0001 Zero 67 (91.8%) 16 (29.6%) >0 6 (8.2%) 38 (70.4%) CSRC_AVG_NORM01 0.0001 Zero 36 (49.3%) 9 (16.7%) >0 37 (50.7%) 45 (83.3%) CDKN1A_AVG_NORM01 0.0001 Zero 48 (65.8%) 16 (29.6%) >0 25 (34.2%) 38 (70.4%) CDKN2A_AVG_NORM01 <0.0001 Zero 57 (78.1%) 23 (42.6%) >0 16 (21.9%) 31 (57.4%) TP53_AVG_NORM01 <0.0001 Zero 59 (80.8%) 16 (29.6%) >0 14 (19.2%) 38 (70.4%) YAP_AVG_NORM01 <0.0001 Zero 68 (93.2%) 22 (40.7%) >0 5 (6.8%) 32 (59.3%) TAZ_AVG_NORM01 <0.0001 Zero 54 (74.0%) 19 (35.2%) >0 19 (26.0%) 35 (64.8%) MITF_AVG_NORM01 <0.0001 Zero 26 (35.6%) 2 (3.7%) >0 47 (64.4%) 52 (96.3%) MLANA_AVG_NORM01 >0 73 (100.0%) 54 (100.0%) TYR_AVG_NORM01 0.2202 Zero 2 (2.7%) 0 (0.0%) >0 71 (97.3%) 54 (100.0%) POSTN_AVG_NORM01 <0.0001 Zero 32 (43.8%) 4 (7.4%) >0 41 (56.2%) 50 (92.6%) FBLN2_AVG_NORM01 <0.0001 Zero 71 (97.3%) 31 (57.4%) >0 2 (2.7%) 23 (42.6%)

TABLE E MM79_CN MM80_CN MM81_CN MM82_CN AVG AVG AVG AVG AVG per per copy per copy per copy per copy copy K14 K14 K14 K14 K14 STDEV % STDEV KRT14_AVG_NORM 1 1 1 1 1 0.000 KRT10_AVG_NORM 2.209 2.229 2.92 3.015 2.593 0.434 17% MITF_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15% MLANA_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15% TYR_AVG_NORM 0.004 0.002 0.002 0.001 0.002 0.001 56% PMEL_AVG_NORM 0.025 0.027 0.03 0.018 0.025 0.005 20% FN1_AVG_NORM 0.077 0.065 0.035 0.042 0.055 0.020 36% SPARC_AVG_NORM 1.294 1.143 0.568 1.707 1.178 0.471 40% AGRN_AVG_NORM 0.004 0.006 0.003 0.002 0.004 0.002 46% THBS1_AVG_NORM 0.064 0.015 0.018 0.005 0.026 0.026 103% THBS2_AVG_NORM 0.366 0.061 0.104 0.057 0.147 0.148 100% THBS4_AVG_NORM 0.018 0.006 0.005 0.001 0.008 0.007 98% VCAN_AVG_NORM 0.095 0.034 0.04 0.027 0.049 0.031 64% BGAN_AVG_NORM 0.015 0.027 0.014 0.015 0.018 0.006 35% COL1A1_AVG_NORM 1.695 3.44 0.689 6.695 3.130 2.635 84% COL4A1_AVG_NORM 0.069 0.026 0.03 0.016 0.035 0.023 66% COL4A2_AVG_NORM 0.115 0.042 0.041 0.004 0.051 0.046 92% COL18A1_AVG_NORM 0.015 0.009 0.005 0.002 0.008 0.006 73% CTGF_AVG_NORM 0.012 0.008 0.016 0.004 0.010 0.005 52% LOX_AVG_NORM 0.029 0.021 0.028 0.021 0.025 0.004 18% LOXL1_AVG_NORM 0.015 0.009 0.016 0.015 0.014 0.003 23% LOXL2_AVG_NORM 0.016 0.011 0.008 0.006 0.010 0.004 42% LOXL3_AVG_NORM 0.003 0.002 0.002 0.001 0.002 0.001 41% LOXL4_AVG_NORM 0.02 0.004 0.003 0.001 0.007 0.009 125% PLOD2_AVG_NORM 0.018 0.014 0.007 0.001 0.010 0.008 75% PLOD1_AVG_NORM 0.069 0.053 0.026 0.017 0.041 0.024 58% SPP1_AVG_NORM 0.092 0.002 0.007 0 0.025 0.045 177% TNC_AVG_NORM 0.025 0.02 0.027 0.013 0.021 0.006 29% PCOLCE2_AVG_NORM 0.011 0.001 0.006 0 0.005 0.005 113% PCOLCE_AVG_NORM 0.028 0.049 0.032 0.04 0.037 0.009 25% PLOD3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113% ITGB3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113% ITGB1_AVG_NORM 0.164 0.054 0.074 0.038 0.083 0.056 68% FBLN2_AVG_NORM 0.049 0.022 0.02 0.016 0.027 0.015 56% CYR61_AVG_NORM 0.006 0.002 0.003 0 0.003 0.003 91% ITGA5_AVG_NORM 0.011 0.005 0.007 0.003 0.007 0.003 53% ITGA3_AVG_NORM 0.016 0.008 0.006 0.008 0.010 0.004 47% ITGA2_AVG_NORM 0.08 0.034 0.019 0.084 0.054 0.033 60% ITGAV_AVG_NORM 0.013 0.005 0.003 0.003 0.006 0.005 79% CSRC_AVG_NORM 0.006 0.003 0.005 0.001 0.004 0.002 59% PTK2_AVG_NORM 0.035 0.02 0.011 0.009 0.019 0.012 63% POSTN_AVG_NORM 0.077 0.092 0.117 0.193 0.120 0.052 43% YAP_AVG_NORM 0.079 0.029 0.033 0.031 0.043 0.024 56% CXCL1_AVG_NORM 0.002 0 0 0 0.001 0.001 200% CSF2_AVG_NORM 0.002 0 0 0 0.001 0.001 200% CCL2_AVG_NORM 0.039 0.018 0.013 0.008 0.020 0.014 70% IL8_AVG_NORM 0.003 0 0.001 0 0.001 0.001 141% IL6_AVG_NORM 0.001 0 0 0 0.000 0.001 200% LAMA3_AVG_NORM 0.038 0.012 0.021 0.011 0.021 0.013 61% TP53_AVG_NORM 0.08 0.04 0.039 0.052 0.053 0.019 36% CDKN1A_AVG_NORM 0.057 0.029 0.037 0.014 0.034 0.018 52% CDKN2A_AVG_NORM 0.003 0.001 0.001 0 0.001 0.001 101% TAZ_AVG_NORM 0.026 0.008 0.008 0.003 0.011 0.010 90% LAMC1_AVG_NORM 0.062 0.013 0.016 0.008 0.025 0.025 101% LAMB1_AVG_NORM 0.046 0.019 0.026 0.008 0.025 0.016 65% LAMA1_AVG_NORM 0.007 0 0.001 0 0.002 0.003 168% LAMC2_AVG_NORM 0.034 0.009 0.012 0.016 0.018 0.011 63% LAMB3_AVG_NORM 0.042 0.016 0.026 0.017 0.025 0.012 48% PLAT_AVG_NORM 0.032 0.02 0.034 0.04 0.032 0.001 27% CSK_AVG_NORM 0.027 0.034 0.021 0.041 0.031 0.001 28% GDF15_AVG_NORM 0.029 0.019 0.033 0.019 0.025 0.001 28% FARP1_AVG_NORM 0.019 0.029 0.022 0.031 0.025 0.001 22% ARPC1B_AVG_NORM 0.015 0.03 0.042 0.018 0.026 0.012 47% NES_AVG_NORM 0.114 0.125 0.112 0.084 0.109 0.017 16% NTRK3_AVG_NORM 0.021 0.025 0.022 0.033 0.025 0.001 25% SNX17_AVG_NORM 0.112 0.099 0.089 0.123 0.106 0.015 14% L1CAM_AVG_NORM 0.017 0.04 0.01 0.024 0.023 0.013 56% CD44_AVG_NORM 0.112 0.089 0.09 0.123 0.104 0.017 16%

The results provided herein demonstrate the development of a method for determining absolute levels (copy numbers) of genes of interest (e.g., FN-associated genes) from paraffin-embedded tissue by generating a highly defined internal standard that can be regenerated indefinitely. This standardization approach can allow for the comparison of results from independent experiments and thus, allows for extensive validation. The RT-PCR not only produced strong signals from highly degraded RNA due to FFPE embedding, but also was amendable to high-throughput analysis and was highly cost effective. While the methods provided herein were validated for melanoma, these methods are likely applicable to other human cancers. The results provided herein also demonstrate the discrimination between benign and malignant pigmented lesions based on multiple markers.

Other Embodiments

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

What is claimed is:
 1. A kit for identifying a malignant skin lesion, wherein the kit comprises: (a) primer pairs for determining, within a test sample, the expression level of the marker genes PLAT, ITGB3, and IL8 to obtain a measured expression level of the marker gene for the test sample, and (b) a control nucleic acid for each of the primer pairs, wherein each control nucleic acid comprises a target nucleic acid for one of the primer pairs, wherein each of the control nucleic acids is cDNA specific for cDNA and not genomic DNA.
 2. The kit of claim 1, wherein the kit further comprises: (c) a primer pair for determining, within a test sample, the expression level of the marker gene GDF15, to obtain a measured expression level of the marker gene for the test sample, and (d) a control nucleic acid for the primer pairs, wherein the control nucleic acid comprises a target nucleic acid for the primer pairs, and wherein the control nucleic acid is cDNA specific for cDNA and not genomic DNA.
 3. The kit of claim 1, wherein the kit further comprises a primer pair for determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample.
 4. The kit of claim 3, wherein the keratinocyte marker gene is K14.
 5. A method for identifying a malignant skin lesion, wherein the method comprises: (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain a measured expression level of the marker gene for the test sample, (b) determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample, (c) removing, from the measured expression level of the marker gene for the test sample, a level of expression attributable to keratinocytes present in the test sample using the measured expression level of the keratinocyte marker gene for the test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for the test sample, and (d) identifying the test sample as containing a malignant skin lesion based, at least in part, on the corrected value of marker gene expression for the test sample.
 6. A kit for identifying a malignant skin lesion, wherein the kit comprises: (a) a primer pair for determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF 15, ITGB 1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain a measured expression level of the marker gene for the test sample, and (b) a primer pair for determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample. 